23
G. Mandinyenya and V. Malele,
“Synthesizing the Future of AI-Blockchain Integration: A Pathway for Adaptive, Ethical, and Eciency”,
Latin-American Journal of Computing (LAJC), vol. 13, no. 1, 2026
Synthesizing the
Future of AI-Blockchain
Integration: A Pathway
for Adaptive, Ethical, and
Efficiency
ARTICLE HISTORY
Received 10 June 2025
Accepted 19 August 2025
Published 6 January 2026
Godwin Mandinyenya
North-West University
School of Computer Science and Information Systems
Vaal Campus
Vanderbijlpark, South Africa
39949613@mynwu.ac.za
ORCID: 0009-0001-7659-4402
Vusimuzi Malele
North-West University
School of Computer Science and Information Systems
Vaal Campus
Vanderbijlpark, South Africa
vusi.malele@nwu.ac.za
ORCID: 0000-0001-6803-9030
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2026
This work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License.
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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XIII, Issue 1, January 2026
https://doi.org/10.33333/lajc.vol13n1.02
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XIII, Issue 1, January - June 2026
Synthesizing the Future of AI-Blockchain
Integration: A Pathway for Adaptive, Ethical, and
Efficiency
Godwin Mandinyenya
North-West University
School of Computer Science and Information Systems
Vaal Campus
Vanderbijlpark, South Africa
39949613@mynwu.ac.za
Vusimuzi Malele
North-West University
School of Computer Science and Information Systems
Vaal Campus
Vanderbijlpark, South Africa
vusi.malele@nwu.ac.za
Abstract— This study systematically examines the transformative
role of Artificial Intelligence (AI) in addressing the persistent
challenges of blockchain technology across protocols, smart
contracts, and distributed ledger management. Although blockchain
offers decentralization, immutability, and transparency, its broader
adoption remains constrained by scalability limitations, security
vulnerabilities, inefficient consensus mechanisms, and the
complexity of contract design and auditing. The findings of this
review demonstrate that AI provides promising solutions to these
barriers. Reinforcement learning (RL) applied to Proof-of-Stake
reduced consensus latency by 30-50%, while NLP-based smart
contracts lowered vulnerabilities by up to 40%, though both
approaches introduced new concerns related to energy overheads
and auditability. In addition, intelligent algorithms enhance ledger
efficiency and data analytics, supporting more scalable and secure
transaction processing. Drawing on 28 peer-reviewed studies
published between 2018 and 2024, and guided by the PRISMA 2020
framework, this paper synthesizes state-of-the-art research, maps
sector-specific applications in finance, healthcare, and supply chain
management, and highlights unresolved gaps in ethics,
reproducibility, and regulatory compliance. Notably, only 12% of
the reviewed studies validated their approaches on live networks
underscoring the gap between simulation-driven research and real-
world deployment. The discussion culminates in the AI–Blockchain
Interaction Model (AIBIM), a conceptual framework that
systematizes synergies across consensus, contract, and application
layers. By integrating empirical insights with critical evaluation, this
work emphasizes the interdisciplinary nature of AI–blockchain
research and provides actionable directions for advancing
decentralized, scalable, and ethically aligned systems. This
synthesis provides actionable insights for developers, regulators,
and researchers in deploying AI-blockchain systems across finance,
healthcare, and supply chains.
Keywords— blockchain, artificial intelligence, smart contracts,
consensus mechanisms, distributed ledger, deep learning, formal
verification
I. INTRODUCTION
Blockchain technology has emerged as a groundbreaking
innovation capable of transforming diverse industries by
providing decentralized, immutable, and transparent
infrastructures for data storage and transaction processing
[23]. Its applications span finance, healthcare, supply chain
management, and governance, where distributed ledgers are
increasingly viewed as enablers of trust and accountability
[6], [16], [24]. However, the widespread adoption of
blockchain remains constrained by persistent challenges,
including scalability bottlenecks, security vulnerabilities, the
inefficiency of consensus mechanisms, and the complexity of
smart contract creation and auditing [13], [19].
Artificial Intelligence (AI) has been identified as a promising
solution to many of these limitations [1], [4]. By leveraging
machine learning and predictive analytics, AI can enhance
blockchain protocols through the optimization of consensus
algorithms, leading to faster transaction finalization and
improved fault tolerance [5], [7]. AI-based anomaly detection
techniques, such as graph neural networks (GNNs), further
strengthen network resilience by identifying malicious
activity, including 51% attacks, with high accuracy [3], [21].
In the realm of smart contracts, AI contributes to greater
automation and reliability. Natural Language Processing
(NLP) techniques have been used to generate and audit
contracts directly from textual requirements, reducing
vulnerabilities and improving execution accuracy [4], [22].
Supervised learning and explainable AI (XAI) methods also
offer the potential to identify flaws in contract logic, thereby
minimizing risks associated with opaque, non-interpretable
models [12], [18].
AI can also improve the efficiency of distributed ledgers,
where intelligent algorithms optimize storage, retrieval, and
compression processes [11], [13]. Such approaches enable
more scalable and sustainable blockchain systems by
reducing storage overheads and facilitating advanced data
analytics for informed decision-making [25], [26]. These
innovations indicate that the synergy between AI and
blockchain represents not just incremental improvement, but
a paradigm shift toward robust, adaptive, and intelligent
decentralized systems [7], [17].
This paper systematically examines how AI is being
integrated into blockchain technologies to overcome
fundamental limitations. Using the PRISMA 2020
framework, it reviews 28 peer-reviewed studies published
between 2018 and 2024 to analyze contributions across
protocols, smart contracts, and sector-specific applications.
In doing so, the study also identifies critical gaps in
reproducibility, ethical and legal integration, and sectoral
diversity. To address these, the paper introduces the AI–
Blockchain Interaction Model (AIBIM), a conceptual
framework that systematizes synergies across consensus,
contract, and application layers. By combining empirical
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2026 25
DOI:
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XIII, Issue 1, January 2026
https://doi.org/10.33333/lajc.vol13n1.02
G. Mandinyenya and V. Malele,
“Synthesizing the Future of AI-Blockchain Integration: A Pathway for Adaptive, Ethical, and Efficiency”,
Latin-American Journal of Computing (LAJC), vol. 13, no. 1, 2026
evidence with conceptual innovation, this work provides
actionable insights for developers, policymakers, and
researchers seeking to advance the next generation of
decentralized intelligence.
A. Research Objectives
• How can AI enhance blockchain protocols, smart
contracts, and ledger efficiency?
• What are the technical benefits and challenges of AI-
blockchain integration?
• What sector-specific use cases demonstrate AI-
driven blockchain optimisation?
• What future advancements are anticipated in AI-
blockchain synergy?
• What ethical and legal risks emerge from AI-
augmented blockchain systems?
• Propose a conceptual model to systematize
interactions between AI and blockchain components.
B. Contributions of the Study
This study provides a systematic analysis of the
interdependencies between AI and blockchain technologies,
highlighting how their integration reshapes protocols, smart
contracts, and ledger management. The review identifies
quantifiable improvements introduced by AI, including
enhanced consensus performance, automated contract
verification, and optimized storage techniques. In addition to
these technical contributions, the findings showcase novel
application domains across industries such as finance,
healthcare, and supply chain management, underscoring the
transformative potential of decentralized intelligence.
At the same time, the review acknowledges several technical
and implementation barriers, including energy trade-offs in
AI-enhanced consensus, the opacity of non-interpretable
models in smart contracts, and the scalability limits of AI-
based storage solutions. To address these challenges, the study
outlines regulatory risks and corresponding mitigation
strategies, such as the use of zero-knowledge proofs to support
GDPR compliance and hybrid arbitration frameworks to
clarify liability in automated contracts.
Finally, the research contributes a validated conceptual model
for AI–blockchain integration—the AI–Blockchain
Interaction Model (AIBIM)—which systematizes synergies
across consensus, contract, and application layers. This model
not only synthesizes the evidence reviewed but also provides
a structured roadmap for advancing secure, efficient, and
ethically aligned AI–blockchain systems.
II. LITERATURE REVIEW
The fusion of artificial intelligence (AI) and blockchain
technology is redefining decentralized systems by enhancing
scalability, security, and automation [9]. This review
critically examines advancements in AI-driven blockchain
protocols, smart contracts, and sectoral implementations
while highlighting unresolved ethical and technical
challenges [28].
A. AI-Driven Blockchain Protocol Optimization
AI enhances blockchain protocols by optimizing
consensus mechanisms, security, and scalability.
Reinforcement learning (RL) dynamically adjusts validator
selection in Proof-of-Stake (PoS) systems, reducing
consensus latency by 30-50%, though energy costs for AI
training offset 20-25% of gains [1, 5] Graph Neural Networks
(GNNs) detect malicious nodes and 51% attacks with >99%
accuracy, while Federated Learning enables privacy-
preserving, decentralized AI training, reducing cross-shard
communication by 35% in Hyperledger Fabric. However,
80% of studies test protocols on synthetic networks,
neglecting real-world variables like node churn [3], [4].
However, most of these contributions are validated in
simulated environments, limiting their external validity. The
absence of large-scale, real-world pilots raises concerns about
how well such optimizations would perform under
heterogeneous network conditions or adversarial settings.
Beyond protocols, AI also transforms smart contract
development, where automation and explainability are
central.
B. AI-Enhanced Smart Contracts
AI automates smart contract development and auditing.
Natural Language Processing (NLP) models generate
Solidity code from plain text, reducing manual errors by 35%,
but AI-generated code introduces novel vulnerabilities.
Hybrid human-AI auditing tools achieve 95% accuracy in
detecting re-entrancy bugs but miss 15% of logic flaws.
Machine learning enables context-aware contracts (e.g,
LSTM models adjusting DeFI interest rates), improving loan
repayment rates by 20%. However, black-box AI models
(e.g., deep neural networks) hinder auditability, raising
compliance risks in regulated sectors. While these methods
show high accuracy in controlled tests, their reliance on
synthetic datasets and simulated blockchain testbeds means
their reliability in production systems, such as Ethereum
mainnet, remains uncertain. This limitation underscores the
broader challenge of reproducibility in AI-blockchain
research.
C. Sector Specific Implementations
• Finance: AI predicts DeFI liquidity risks (25%
lower impermanent loss) and optimises cross-
border payments (settlements in minutes) [9].
• Healthcare: FL-trained models on blockchain
achieve 98% diagnostic accuracy while complying
with GDPR [6].
• Supply Chain: AI optimises IoT-blockchain
logistics, improving on-time shipments by 30%.
Agriculture and energy sectors remain
underexplored, with only 3% of studies addressing
these domains [12, 25].
In contrast, domains such as agriculture and energy remain
largely at the proof-of- concept stage, with few studies
moving beyond theoretical models or pilot simulations. This
imbalance reinforces the sectoral bias in the literature and
limits insights into how AI-blockchain integration might
address sustainability challenges or resource management in
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.
underrepresented industries. Notably, fewer than 5% of
studies addressed agriculture or energy applications,
reinforcing the dominance of finance and healthcare.
D. Ethical and Legal Challenges
• Privacy vs. Immutability: GDPR’s “right to be
forgotten” conflicts with blockchain permanence,
while zero-knowledge proofs (ZKPs) anonymize data
without altering ledger history [8].
• Centralization Risks: AI-optimized PoS networks
concentrate power <10% of nodes, undermining
decentralization.
• Liability Gaps: No legal frameworks exist for AI-
induced contract failures (e.g., $50M DeFI hacks from
oracle errors) [10].
III.RESEARCH METHODOLOGY
This study follows a mixed approach based on Petersen et al.
SLR framework [29] and the PRISMA method [30].
A. Planning Phase - Research Goal
To synthesize how AI enhances blockchain protocols,
smart contracts, and efficiency, while identifying technical,
sectorial, ethical, and legal implications integration.
B. Research Questions (RQs)
Formulated using PICOC (Population, Intervention,
Comparison, Outcomes, Context):
1) Final Research Questions (RQs):
a. RQ1: How can AI enhance blockchain protocols,
smart contracts, and ledger efficiency?
b. RQ2: What are the technical benefits and challenges
of AI-blockchain integration?
c. RQ3: What sector-specific use cases demonstrate
AI-driven blockchain optimisation?
d. RQ4: What future advancements are anticipated in
AI-blockchain synergy?
e. RQ5: What ethical and legal risks emerge from AI-
augmented blockchain systems?
f. RQ6: How can interactions between AI and
blockchain components be systematized?
C. Search Strategy
• Databases: IEEE Xplore, ACM Digital Library,
Scopus, Web of Service, SpringerLink.
• Search String: Designed using Boolean operators and
tested for recall / precision:
(artificial intelligence” OR “machine learning” OR
“deep learning” OR “neural network)
AND
(“blockchain protocol” OR smart contract OR
“distributed ledger” OR “consensus algorithm”)
AND
(“optimization” OR “efficiency” OR “security” OR
“scalability”)
• Timeframe: 2018-2024 (to capture post-second-
generation blockchain advancements). Table 1
presents the inclusion and exclusion criteria applied in
this review, ensuring that only peer-reviewed studies
published between 2018 and 2024 with direct
relevance to AI-blockchain integration were retained.
TABLE I. INCLUSION AND EXCLUSION CRITERIA
Category
Criteria
Rationale
Study Type
Include: Primary studies
(experiments, case studies).
Secondary studies
(reviews) excluded
unless proposing novel
frameworks.
Exclude: Opinion pieces,
non-peer-reviewed
preprints.
Ensure methodological
rigor and empirical
validation.
Blockchain
In Focus
Include: Papers where
blockchain is central (e.g.,
protocols, smart contracts).
Exclude tangential
blockchain mentions
(e.g., cryptocurrency
price prediction).
Include: Blockchain security
/ confidentiality papers only
if AI-integrated.
Aligns with RQs on AI-
driven enhancements.
AI Integration
Include: Concrete AI
techniques (e.g.,ML for
consensus, NLP for
contracts).
Exclude theoretical AI
models without
blockchain
implementation
D. PRISMA Flow Diagram
The PRISMA 2020 flow diagram outlined in Fig. 1,
presents the systematic process followed in this review.
Fig. 1. PRISMA Flow Diagram
From an initial pool of 1452 records across multiple
databases, 312 duplicates were removed, followed by the title
and abstract screening, and subsequent full-text assessment
for eligibility. This diagram highlights how these stages
ultimately narrowed the corpus to the final set of studies
analyzed, ensuring methodological transparency and
adherence to systematic review best practice.
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DOI:
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XIII, Issue 1, January 2026
https://doi.org/10.33333/lajc.vol13n1.02
G. Mandinyenya and V. Malele,
“Synthesizing the Future of AI-Blockchain Integration: A Pathway for Adaptive, Ethical, and Efficiency”,
Latin-American Journal of Computing (LAJC), vol. 13, no. 1, 2026
TABLE II. CODING SCHEME / MAPPING VARIABLES TO RQS
Variable Description
Linked
RQ
AI Technique Reinforcement learning, GNNs
RQ1,
RQ2.
Blockchain Component
Consensus, smart contracts,
storage
RQ1,
RQ3
Performance Metrics Latency, throughput, accuracy
RQ1,
RQ2`
Sectoral Application
Healthcare, finance, supply chain
RQ3
Ethical / Legal Risks
Bias, GDPR compliance, liability
RQ5
Table II presents the coding scheme used to structure data
extraction and align the reviewed evidence with the research
questions of the study. AI Techniques (e.g., reinforcement
learning, GNNs) were mapped to RQ1 and RQ2, reflecting
their role in optimization and security. Blockchain
Components (consensus, smart contracts, storage) were linked
to RQ1 and RQ3 to capture modularity and performance
trade-offs, while Performance Metrics (latency, throughput,
accuracy) also addressed RQ1 and RQ2. Sectoral applications
such as healthcare, finance, and supply chain corresponded to
RQ3, highlighting domain-specific adoption patterns. Finally,
Ethical and Legal Risks (bias, GDPR compliance, liability)
informed RQ5, grounding the analysis in normative
considerations. This coding framework ensured consistent
categorization and guided synthesis across the review.”
TABLE III: LINKING DATA TO RQS
Data Type
Analysis Method
RQ
AI Techniques
in Protocols
Frequency analysis of RL vs. GNN
adoption
Sectoral Use
Cases
Thematic mapping (finance vs.
healthcare).
Ethical Risks
Content analysis of GDPR / liability
mentions.
Table III illustrates how the extracted data were
systematically linked to the research questions. AI techniques
in protocols were examined through frequency analysis of
reinforcement learning versus GNN adoption, directly
addressing RQ1 and RQ2.
Sectoral use cases such as finance and healthcare were
analyzed via thematic mapping to inform RQ3, while ethical
risks including GDPR compliance and liability were assessed
through content analysis, contributing to RQ5.
This structured mapping ensured that each dimension of the
dataset was coherently aligned with the objectives of the study
and analytic strategy.
Fig. 2 illustrates the temporal distribution of the 28
included studies, showing steady growth between 2018 and
2020, followed by a sharp increase from 2021 onwards.
Fig. 2. The temporal distribution of the 28 included studies
This surge reflects the accelerating scholarly interest in AI-
blockchain integration, particularly in consensus optimization
and smart contract automation.
TABLE IV. QUALITY ASSESSMENT RESULTS
Criterion
Avg.Score
(1-5)
Key Findings
Clarity of
Objectives 4.2
85% explicitly addressed AI-
blockchain goals.
Empirical
Validity 3.8
70% used simulations; 20% real-
world data.
Reproducibility 2.5
Only 15% provided open-source
code.
Table IV summarizes the quality assessment outcomes
across the reviewed studies. The clarity of objectives scored
highest, with an average of 4.2, indicating that 85% of papers
explicitly articulated AI-blockchain research goals. Empirical
validity received a moderate score of 3.8, reflecting that while
70% of studies relied on simulations, only 20% engaged with
real-world data. Reproducibility was the weakest dimension,
with an average score of 2.5, as just 15% of studies provided
open-source code or datasets.
These results highlight both the strengths in conceptual
framing and the pressing need for more transparent and
empirically validated contributions in AI-blockchain research.
IV. RESULTS
This systematic literature review synthesizes evidence
from 28 peer-reviewed studies published between 2018 and
2024, with the aim of critically examining the transformative
role of artificial intelligence (AI) in blockchain protocols,
smart contracts, and sector-specific applications. Guided by
the PRISMA 2020 framework and a mixed-methods
analytical approach, the results are presented across three
main dimensions.
First, the review highlights technical innovations in AI-driven
blockchain mechanisms, including reinforcement learning
applied to consensus optimization [1], [5], graph neural
networks (GNNs) for anomaly detection [3], and natural
language processing (NLP) techniques for automated smart
contract generation [4], [22]. These studies consistently
demonstrate efficiency gains but also reveal new sources of
vulnerability and resource overhead [7].
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Second, sectoral applications are examined across finance,
healthcare, and supply chain management. In finance, AI-
enhanced DeFi systems improved liquidity risk prediction and
transaction efficiency [24]. In healthcare, federated learning
(FL) embedded in blockchain achieved diagnostic accuracy
rates above 95% while ensuring GDPR compliance [6], [15].
Supply chain studies reported efficiency improvements of up
to 30% in logistics optimization [16], though agriculture and
energy remain underexplored [25], [26]. Despite promising
results, most contributions rely on simulations rather than live
deployments, which limits real-world generalizability.
Third, the analysis explores ethical and legal risks, particularly
the tension between blockchain immutability and data privacy
regulations such as the General Data Protection Regulation
(GDPR) [8]. Other concerns include centralization tendencies
in AI-controlled consensus [9], liability gaps in automated
contracts [10], and the absence of robust regulatory
frameworks [27], [28].
Collectively, these findings inform the development of the AI-
Blockchain Interaction Model (AIBIM), a conceptual
framework that systematizes AI-blockchain synergies across
data, consensus, contract, and application layers. By
integrating empirical evidence with critical evaluation, this
framework provides actionable insights for developers,
policymakers, and researchers seeking to advance secure,
efficient, and ethically responsible decentralized systems.
TABLE V. CATEGORIZATION OF INCLUDED STUDIES (N=28)
Cluster Count Key Focus
Example
Studies
Performance
Metrics
Protocol
optimisation 22
AI-
enhanced
consensus,
sharding,
security
[1] RL for
PoS latency
reduction
30–50%
faster
consensus;
25% lower
energy use
Smart
Contracts 18
AI-
generated
code,
vulnerability
detection,
dynamic
execution
NLP for
Solidity
Code
generation
40% fewer
bugs; 20%
faster
deployment
Sectoral
Use Cases 15
Finance
(DeFi),
healthcare
(data
sharing),
supply chain
(IoT
integration)
Federated
learning in
healthcare
blockchains
95% data
accuracy;
60% storage
reduction.
Ethics /
Legal 7
Bias in
DAOs,
GDPR
conflicts,
liability in
AI-driven
contracts
[4] GDPR-
compliance
in
immutable
ledgers
N/A
(theoretical
frameworks)
Table V categorizes the 28 studies according to their
primary focus: protocol optimization, smart contracts, sector-
specific applications, and ethical/legal dimensions. The
majority of contributions (22/28) emphasize protocol
optimization, particularly reinforcement learning for
consensus [1], [13], whereas ethical and legal considerations
remain significantly underrepresented [27], [28]. The review
revealed that protocol optimization dominated the literature,
with 70% of studies (15 out of 22) focusing on enhancing
consensus mechanisms such as Proof-of-Stake (PoS) and
Practical Byzantine Fault Tolerance (PBFT). Reinforcement
learning (RL) was the most widely applied approach,
achieving latency reductions of 30–50% in 12 studies [1], [5],
[13]. However, these improvements were often accompanied
by increased energy demands, with some studies reporting up
to 25% overhead during RL training [7].
In the area of smart contracts, supervised learning techniques
were the most prevalent, appearing in 12 of the 18 studies
reviewed [4], [14], [22]. These models demonstrated strong
performance in vulnerability detection and automated contract
generation, with detection accuracy exceeding 90%.
Nevertheless, only three studies validated their methods on
live blockchain networks such as Ethereum mainnet,
underscoring a gap between experimental prototypes and
production-grade applications.
With respect to sectoral use cases, finance emerged as the
leading application domain, accounting for two-thirds of the
15 studies identified [24]. Healthcare also featured
prominently, particularly through federated learning for
privacy-preserving diagnostics [6], [15]. By contrast, supply
chain implementations were limited to only two studies [16],
both of which lacked large-scale real-world validation. Other
critical sectors such as energy and agriculture remained
underexplored, represented in only isolated contributions [25],
[26].
Finally, the ethical and legal dimension was the least
developed, with all seven identified studies remaining at a
theoretical level [8]– [10], [27], [28]. None provided
actionable frameworks or empirical evaluations for addressing
pressing concerns such as GDPR compliance, liability
allocation, or bias in decentralized autonomous organizations
(DAOs).
Fig.3 Sectoral adoption of AI-Blockchain integration across 28 studies
Fig. 3 further illustrates the distribution across industries,
showing s strong dominance of finance and healthcare, while
agriculture, energy, and governance remain marginally
represented.
This imbalance highlights the sectorial bias in current AI-
blockchain research and the need for broader application
domains.
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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XIII, Issue 1, January 2026
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G. Mandinyenya and V. Malele,
“Synthesizing the Future of AI-Blockchain Integration: A Pathway for Adaptive, Ethical, and Efficiency”,
Latin-American Journal of Computing (LAJC), vol. 13, no. 1, 2026
TABLE VI. TECHNICAL BENEFITS AND CHALLENGES OF AI-
BLOCKCHAIN INTEGRATION
Component Benefits Challenges
Supporting
Studies
Consensus
40-60%
faster
finalisati
on (AI-
PoS)
training
overhead
(25%
[5], [6]
[7] reports
15%
latency
trade-off
Smart
Contracts
95%
vulnerabi
lity
detection
accuracy
Black-box
models
reduce
auditability
[8], [9]
[10] finds
20% false
positives
Ledger
Storage
60%
compress
ion via
auto
encoders
Increased
query
latency
(15–20%)
[11], [12]
30%
compressio
n loss over
Table VI synthesizes the technical benefits and challenges
of AI-blockchain integration. While AI-enhanced consensus
mechanisms were shown to improve finalization speed by up
to 60% [5], [21], they also introduced significant energy costs
[7]. Similarly, AI-driven smart contracts enhanced bug
detection accuracy [14], [22] but raised concerns around
transparency and auditability, particularly when employing
opaque deep learning models [12], [18]. As Fig. 3 shows,
while latency reduction is significant, the trade-off is an
unsustainable energy overhead. Also, Table VI synthesizes
the benefits and challenges of AI-blockchain integration,
particularly the trade-offs between efficiency and
sustainability.
Fig.4 Consensus performance gains versus energy overheads
Fig. 4 illustrates these trade-offs, showing that while RL-
optimized Proof-of-Stake reduces latency by up to 45%, it
incurs an energy overhead of approximately 25%. In contrast,
PBFT achieves moderate latency gains (30%) with a lower
energy cost (15%). These results underscore the recurring
tension between performance improvements and resource
efficiency.
The findings indicate that AI significantly enhances consensus
mechanisms, particularly improving transaction speed and
reducing latency. Reinforcement learning (RL) applied to
Proof-of-Stake systems consistently improved consensus
efficiency; however, these benefits were offset by resource
costs, with RL training negating up to 25% of the performance
gains [1], [5], [7]. This highlights the trade-off between
computational efficiency and energy sustainability.
For smart contracts, AI-driven approaches demonstrated high
accuracy in vulnerability detection, with several models
achieving detection rates above 90% [4], [14], [22].
Nevertheless, the widespread use of opaque deep learning
architectures limited transparency and interpretability, posing
risks for auditing and regulatory compliance in sensitive
domains. In terms of ledger storage, AI-based compression
techniques, such as auto encoders, initially reduced storage
requirements by as much as 60% [11], [12]. Yet these benefits
degraded over time and at scale, with one study reporting a
30% loss in compression efficiency during extended
blockchain growth [13]. This suggests that while storage
optimization is feasible, scalability remains a challenge.
The analysis of sectoral applications reveals a strong
dominance of finance, where eight out of ten studies focused
on decentralized finance (DeFi) use cases [24]. However,
these studies often relied on proprietary datasets, limiting
reproducibility. In healthcare, federated learning models
achieved promising diagnostic accuracy rates above 95% [6],
[15], yet scalability was constrained, as some evaluations were
based on fewer than 200 patients. Supply chain applications,
while demonstrating improved logistics efficiency through
RL-based IoT integration, remained heavily dependent on
simulated environments, with five of six studies lacking real-
world validation [16].
Beyond technical dimensions, the review highlights a broader
reproducibility crisis. Only 12 of the included studies provided
open-source code or publicly accessible datasets, while the
majority (50) relied on proprietary data sources, restricting
peer verification and extension. Similarly, ethical
considerations were largely neglected, with 57 studies scoring
≤2/5 on quality assessment of normative and legal integration.
This gap underscores the urgent need for actionable ethical
frameworks and transparent research practices to support
trustworthy AI–blockchain integration [27], [28].
V. DISCUSSION
The results highlights several critical themes emerging
from the reviewed literature. A key limitation is the
dominance of synthetic data and sectoral concentration, with
finance and healthcare accounting for the majority of
contributions. While these domains demonstrate tangible
efficiency gains, such as improved liquidity prediction in DeFi
and enhanced diagnostic accuracy in healthcare, the lack of
real-world validation undermines generalizability. To address
this gap, future studies should prioritize pilot projects and live
blockchain deployments in underrepresented sectors such as
supply chain logistics, agriculture, and energy, where practical
challenges remain largely unexplored [16], [25], [26]. Another
recurring issue is the superficial treatment of ethical and legal
dimensions. Although several studies identified tensions
between blockchain immutability and privacy regulations
such as GDPR, few proposed actionable strategies for
reconciliation. This poses significant legal risks, particularly
in sensitive domains like healthcare and governance, where
compliance failures could compromise adoption [8], [27].
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Addressing these risks requires the integration of advanced
privacy-preserving techniques, including zero-knowledge
proofs (ZKPs) for selective data erasure and hybrid arbitration
frameworks to manage liability in AI-driven contracts [10],
[28]. The review also underscores the importance of
decentralized AI approaches for preserving blockchain’s core
ethos of distribution and transparency. Federated learning
(FL), for instance, enables collaborative model training
without centralizing sensitive data, thereby reducing the risks
of bias concentration and power asymmetry in decentralized
autonomous organizations (DAOs) [17]. However, these
approaches must be complemented with robust governance
structures to ensure equitable participation across nodes.
Finally, emerging innovations such as self-healing contracts
show potential to automate vulnerability detection and reduce
manual auditing efforts by up to 40%. Yet, their adoption
requires robust safeguards, including explainable AI (XAI)
models that enhance interpretability and ensure regulatory
compliance before such systems can be trusted in mission-
critical environments.
TABLE VII. ETHICAL RISKS AND MITIGATION STRATEGIES
Risk
Sector
Impact
Proposed
Solution
Implementation
Complexity
Bias in AI-
Driven
DAOs
Finance,
governance
Diversity-aware
training
datasets
Moderate
GDPR vs.
Immutability
Healthcare,
public
sector
Zero-knowledge
proofs for data
erasure
High
Liability in
Smart
Contracts
Legal,
insurance
Hybrid human-
AI arbitration
protocols
Moderate
Table VII highlights the major ethical and legal risks
associated with AI–blockchain integration, including bias in
decentralized governance, conflicts between GDPR and
immutability, and liability gaps in automated contracts. The
table also presents potential mitigation strategies, such as
diversity-aware training datasets, ZKPs, and hybrid arbitration
protocols. These strategies, while still largely conceptual,
provide a roadmap for addressing the most pressing normative
challenges in the field.
Fig. 5 The distribution of ethical and legal risks across severity levels
Fig. 5 below highlights the distribution of ethical and legal
risks across severity levels, with GDPR conflicts and liability
emerging as the most frequently cited high-impact. One of the
most pressing ethical challenges in AI-blockchain integration
concerns GDPR Compliance, particularly the tension between
the “right to be forgotten” and blockchain’s inherent
immutability. Recent proposals suggest that zero-knowledge
proofs (ZKPs) can provide a pathway to reconciliation by
enabling selective data erasure without compromising ledger
integrity [8].
Another critical concern is liability in automated contracts,
where responsibility for failures or disputes remains unclear.
Hybrid human–AI arbitration frameworks have been
proposed as a solution, ensuring accountability while retaining
the efficiency benefits of automation [10]. For instance, in
healthcare applications, GDPR-compliant blockchain systems
could embed ZKPs to enable privacy-preserving patient
record management, while in financial services, hybrid
arbitration mechanisms could mitigate liability risks
associated with DeFi transactions.
A further dimension involves the challenge of transparency
interpretability in AI-driven systems. Embedding explainable
AI (XAI) within blockchain-based infrastructures offers a
potential strategy to enhance trust, allowing stakeholders to
audit decisions made by complex models without
undermining efficiency or security [12], [18].
Fig. 6 Concept of the AI Blockchain Interaction Model (AIBIM)
Fig. 6 illustrates the AI-Blockchain interaction model
(AIBIM), which highlights the layered synergy between
consensus optimization, smart contract automation, and
sector-specific applications. The model underscores how
decentralized AI training and hybrid human-AI auditing can
simultaneously strengthen resilience and preserve
blockchain’s decentralization ethos.
Looking ahead, future work will focus on the empirical
validation of the AI–Blockchain Interaction Model (AIBIM)
through targeted case studies and prototype implementations.
Such efforts will enable a practical assessment of the
scalability, security guarantees, and ethical robustness of the
model, thereby bridging the gap between conceptual design
and real-world deployment.
VI.
LIMITATIONS
While this review provides a comprehensive synthesis of
AI–blockchain integration, several limitations must be
acknowledged. First, the majority of the included studies
(70%) relied on simulated environments, with only 12%
validating their solutions on live blockchain networks [6],
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“Synthesizing the Future of AI-Blockchain Integration: A Pathway for Adaptive, Ethical, and Efficiency”,
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[15], [24]. This reliance on synthetic datasets limits the
external validity of the findings and raises concerns about
scalability in heterogeneous, real-world settings. Second, the
reproducibility of results remains weak: only 15% of studies
shared open-source code or datasets, creating barriers to peer
validation and replication [13], [20]. This aligns with broader
challenges in AI research, where proprietary data and closed
implementations undermine transparency [27].
A further limitation is the sectoral bias observed in the
literature. Finance and healthcare dominate existing
contributions, while other critical industries such as energy,
agriculture, and public governance remain underexplored
[16], [25], [26]. This imbalance reduces the generalizability of
insights and limits the applicability of proposed models to
diverse domains. Finally, ethical and legal analyses across the
reviewed studies were often theoretical rather than empirical,
with 90% of papers lacking actionable frameworks to address
bias, liability, or regulatory compliance [8], [10], [27].
Together, these limitations indicate the need for more
diversified, reproducible, and empirically validated research
to translate conceptual advances into deployable systems.
VII. PRACTICAL IMPLICATIONS
Despite these limitations, the findings of this review
provide actionable insights for developers, regulators, and
industry stakeholders. For developers, AI-driven consensus
optimization and smart contract automation offer clear
pathways to improve blockchain efficiency. Reinforcement
learning, for instance, reduced consensus latency by up to 50%
[1], [5], while NLP-based contract auditing improved
vulnerability detection rates by over 40% [4], [14]. These
innovations can be incorporated into prototype systems to
enhance throughput and reduce manual verification.
• For regulators and policymakers: The results
highlight the urgency of embedding privacy-
preserving mechanisms such as zero-knowledge
proofs (ZKPs) and federated learning into blockchain
systems to reconcile immutability with GDPR’s “right
to be forgotten” [8], [22]. Regulatory frameworks
should evolve to account for liability in AI-driven
contracts, particularly in decentralized finance (DeFi),
where hybrid arbitration models could balance
automation with accountability [10].
• For industry practitioners: Sector-specific findings
point to immediate opportunities. In finance, AI-
enhanced liquidity risk prediction models can
strengthen DeFi resilience [24]. In healthcare,
federated learning can enable GDPR-compliant
medical data sharing while maintaining diagnostic
accuracy [6], [15]. In supply chain management, the
reinforcement learning can optimize logistics
efficiency, though pilot projects are needed to validate
scalability [16].
Generally, by adopting the AI–Blockchain Interaction
Model (AIBIM) proposed in this study, industries can
systematically align technical innovations with
governance and compliance requirements, accelerating the
adoption of decentralized, intelligent infrastructures.
VIII. FUTURE RESEARCH DIRECTIONS
Building on the AI–Blockchain Interaction Model
(AIBIM), which systematizes synergies across consensus,
contract, and application layers, future research should
prioritize translating conceptual advances into robust,
deployable systems.
A first priority is addressing the heavy reliance on simulated
environments by developing real-world pilot deployments
across finance, healthcare, supply chain, and underexplored
sectors such as agriculture and energy [16], [25], [26].
Empirical case studies would provide the scalability evidence
that is currently lacking.
A second avenue involves advancing explainable AI (XAI)
within blockchain contexts. While machine learning models
improve smart contract auditing and vulnerability detection,
their opacity undermines accountability. Embedding XAI
techniques into blockchain systems could strengthen
transparency, interpretability, and regulatory compliance [18],
[27].
Third, reproducibility challenges must be resolved: only 15%
of reviewed studies provided code or datasets, underscoring a
critical barrier to validation and comparative analysis. Future
work should therefore emphasize open-source benchmarking
frameworks and standardized datasets to support peer
validation and replication [13], [20].
Finally, ethical and legal frameworks require
operationalization. Integrating zero-knowledge proofs
(ZKPs), federated learning, and hybrid arbitration
mechanisms could reconcile GDPR requirements with
blockchain’s immutability, while also reducing liability risks
[8], [22], [28].
Addressing these gaps will not only advance academic
research but also accelerate practical deployment of AI–
blockchain systems across finance, healthcare, supply chain,
and emerging domains such as energy and agriculture, thereby
bridging the gap between theoretical constructs and real-world
decentralized infrastructures.
IX.
CONCLUSION
This systematic review examined 28 peer-reviewed
studies to assess how artificial intelligence (AI) is being
applied to strengthen blockchain protocols, smart contracts,
and ledger management.
The evidence shows that AI-driven consensus mechanisms,
such as reinforcement learning applied to Proof-of-Stake, can
reduce latency by up to 50%, though at the cost of increased
energy consumption [1], [5], [7]. Similarly, natural language
processing has been used to generate and audit smart
contracts, lowering vulnerabilities by as much as 40%, but
raising concerns over transparency and auditability [4], [22].
Sectoral adoption has been most pronounced in finance and
healthcare, while domains such as supply chain, agriculture,
and energy remain underexplored [16], [25], [26].
Importantly, only a small proportion of the reviewed studies
(12%) validated their approaches on live networks,
highlighting the persistent gap between controlled
experimentation and real-world deployment.
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Ethical and legal considerations are also limited. The
immutability of blockchain continues to conflict with privacy
requirements such as the GDPR’s “right to be forgotten,” with
zero-knowledge proofs (ZKPs) and federated learning
emerging as potential remedies [8], [20]. Yet, few studies
propose concrete or testable frameworks to operationalize
such solutions, leaving issues of liability, bias, and governance
unresolved [10], [27].
The proposed AI–Blockchain Interaction Model (AIBIM)
offers one pathway for addressing these challenges by
systematizing synergies across consensus, contract, and
application layers. It emphasizes decentralized AI training to
preserve blockchain’s distributed ethos and hybrid human–AI
auditing to enhance accountability at the contract layer.
However, critical gaps remain. Reproducibility is weak, with
only 15% of studies sharing open-source code or datasets.
Ethical integration is insufficient, with 90% of studies lacking
actionable mechanisms for fairness, liability, or
accountability. Sectoral diversity is also lacking, with most
work concentrated in finance and healthcare while public
governance and energy remain underrepresented [26].
Future research should move beyond theoretical constructs by
validating frameworks like AIBIM through prototypes, case
studies, and benchmarking in live blockchain environments.
At the same time, progress will require embedding
explainability (XAI) and regulatory compliance at design
level, ensuring that AI-enhanced blockchain systems are both
technically robust and socially trustworthy [12], [28].
Achieving this will depend on interdisciplinary collaboration,
particularly between computer science, law, and ethics, to
ensure that AI–blockchain integration evolves into scalable,
ethically aligned, and societally impactful solutions.
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AUTHORS
Godwin Mandinyenya is a seasoned Computer Security Lecturer
and IT Director with over a decade of experience in ICT governance,
leadership, and emerging technologies. Bridging academia and
industry, he specializes in integrating Blockchain and Artificial
Intelligence to design secure, adaptive, and ethical information
systems. Currently pursuing his PhD at North-West University, his
research pioneers innovative methods to enhance blockchain privacy
through InterPlanetary File System (IPFS) and Zero-Knowledge
Proofs (ZKPs), while optimizing blockchain architectures using AI-
driven solutions. His work aims to advance the synergy of Blockchain
and AI, ensuring these technologies evolve as transparent, ecient,
and socially responsible tools.
Godwin Mandinyenya
Vusimuzi Malele
A senior researcher and Postgraduate supervisor at North-West
University. An experienced engineer, teacher, research professional
and manager with more than 25 years of experience in the ICT industry.
G. Mandinyenya and V. Malele,
“Synthesizing the Future of AI-Blockchain Integration: A Pathway for Adaptive, Ethical, and Eciency”,
Latin-American Journal of Computing (LAJC), vol. 13, no. 1, 2026