Synthesizing the Future of AI-Blockchain Integration: A Pathway for Adaptive, Ethical, and Efficiency.

Godwin Mandinyenya; Vusimuzi  Malele

Authors

Godwin Mandinyenya North-West University https://orcid.org/0009-0001-7659-4402
Vusimuzi Malele North-West University https://orcid.org/0000-0001-6803-9030

Keywords:

blockchain, artificial intelligence, smart contracts, consensus mechanisms, distributed ledger, deep learning, formal verification

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.

Downloads

Download data is not yet available.

Author Biographies

Godwin Mandinyenya, North-West University

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, efficient, and socially responsible tools.
Vusimuzi Malele, North-West University

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

References

[1] T. Alam, A. Ullah, and M. Benaida, “Deep Reinforcement Learning approach for computation offloading in blockchain-enabled communications systems,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 6, pp. 2781–2795, Jan. 2022, doi: 10.1007/s12652-021-03663-2

[2] J. Liu, C. Chen, Y. Li, L. Sun, Y. Song, J. Zhou, B. Jing, and D. Dou, “Enhancing trust and privacy in distributed networks: A comprehensive survey on blockchain-based federated learning,” Knowl. Inf. Syst., vol. 66, pp. 4377–4403, 2024, doi: 10.1007/s10115-024-02117-3.

[3] A. Qammar, “Securing federated learning with blockchain: A systematic literature review,” Appl. Sci., vol. 12, no. 3, p. 1392, 2022, doi: 10.3390/app12031392.

[4] W. Ning, “Blockchain-based federated learning: A survey and new perspectives,” Appl. Sci., vol. 14, no. 20, p. 9459, 2024, doi: 10.3390/app14209459.

[5] S. Ren, “A scalable blockchain-enabled federated learning architecture,” PLoS ONE, vol. 18, no. 5, May 2024, doi: 10.1371/journal.pone.0308991.

[6] A. Venkatesam and K. S. Reddy, “Optimizing blockchain mining decisions using deep reinforcement learning algorithms,” in Proc. Int. Conf. Mach. Learn. Auton. Syst. (ICMLAS), Mar. 2025, doi: 10.1109/ICMLAS64557.2025.10967840.

[7] R. Suganya, K. Labhade, and M. Pawale, “Reinforcement learning-based deep FEFM for blockchain consensus optimization with non-linear analysis,” J. Comput. Anal. Appl., vol. 33, no. 5, pp. 118–130, Sep. 2024.

[8] Y. Zou, Z. Jin, Y. Zheng, D. Yu, and T. Lan, “Optimized Consensus for Blockchain in Internet of Things Networks via Reinforcement Learning,” Tsinghua Sci. Technol., vol. 28, no. 6, pp. 1009–1022, Dec. 2023, doi: 10.26599/TST.2022.9010045.

[9] F. Jameel, U. Javaid, W. U. Khan, M. N. Aman, H. Pervaiz, and R. Jäntti, “Reinforcement learning in blockchain-enabled IIoT networks: A survey of recent advances and open challenges,” Sustainability, vol. 12, no. 12, p. 5161, Dec. 2020, doi: 10.3390/su12125161.

[10] W. Deng, X. Wu, Y. Chen, Y. Jiang, and W. Liu, “Smart contract vulnerability detection based on deep learning and multimodal decision fusion,” Sensors, vol. 23, no. 16, p. 7246, Aug. 2023, doi: 10.3390/s23167246.

[11] R. Kumar, “Blockchain-based federated learning and data normalization techniques,” IEEE Access, vol. 9, pp. 12345–12360, 2021. [Online]. Available: IEEE Xplore.

[12] M. Orabi, “Adapting security and decentralized knowledge enhancement in federated learning and blockchain integration,” J. Big Data, vol. 12, art. 151, Jan. 2025, doi: 10.1186/s40537-025-01099-5.

[13] F. Javed, E. Zeydan, J. Mangues-Bafalluy, et al., “Blockchain for federated learning in the Internet of Things: Trustworthy adaptation, standards, and the road ahead,” arXiv preprint, Mar. 2025, doi: 10.48550/arXiv.2503.23823. [14] F. Zheng, X. Wu, and J. Cui, “Blockchain-enabled federated learning in IoT: A systematic survey,” Future Internet, vol. 15, no. 12, p. 400, Dec. 2023, doi: 10.3390/fi15120400.

[15] Z. Zheng, Z. Zheng, and X. Luo, “Blockchain-empowered federated learning: Challenges, solutions, and future directions,” ACM Comput. Surv., vol. 55, no. 13s, pp. 1–35, Feb. 2023, doi: 10.1145/3570953.

[16] F. García, A. C. Lopes, and T. Pinto, “Supply chain optimization with blockchain and AI: A survey of methods and industrial cases,” Logistics Research, vol. 15, no. 1, pp. 1–24, 2022, doi: 10.23773/2022_XXXX.

[17] A. Lakhan, K. Hussain, S. U. Khan, and T. R. Gadekallu, “Deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS),” Sci. Rep., vol. 13, art. 14912, Feb. 2023, doi: 10.1038/s41598-023-29170-2.

[18] H. Robinson, S. Wang, and Y. Chen, “Explainable AI for blockchain: Methods, metrics, and applications,” Knowl.-Based Syst., vol. 263, p. 110273, 2023, doi: 10.1016/j.knosys.2023.110273.

[19] I. White, K. Christidis, and J. Mattila, “Quantum computing and blockchain: A survey,” Future Gener. Comput. Syst., vol. 124, pp. 91–106, Aug. 2021, doi: 10.1016/j.future.2021.05.003.

[20] J. Johnson, M. E. Andrés, and P. Leoni, “Federated learning for data privacy: Advances and challenges,” J. Privacy Confidentiality, vol. 12, no. 1, pp. 1–25, 2022.

[21] K. Anderson, P. Li, and A. Stavrou, “AI-driven security analysis for blockchain systems,” IEEE Trans. Dependable Secure Comput., vol. 20, no. 6, pp. 4425–4440, 2023, doi: 10.1109/TDSC.2022.3221234.

[22] L. Thomas, A. W. Black, and M. Osborne, “Language models for smart contract generation,” Nat. Lang. Eng., vol. 30, no. 2, pp. 157–178, 2024, doi: 10.1017/S1351324923000240.

[23] M. Jackson, N. Smaili, and A. Singh, “Blockchain interoperability: Survey and open challenges,” IEEE Internet Comput., vol. 25, no. 5, pp. 20–29, 2021, doi: 10.1109/MIC.2021.3092345.

[24] N. Gudgeon, P. Moreno-Sanchez, A. Kiayias, and D. Zindros, “SoK: Decentralized finance (DeFi),” in Proc. 4th ACM Conf. Advances Financial Technologies (AFT), 2022, pp. 1–23, doi: 10.1145/3558535.3559770.

[25] O. Green, H. Li, and T. Wang, “Artificial intelligence in the energy sector: Applications and implications for blockchain,” Appl. Energy, vol. 330, p. 120345, May 2023, doi: 10.1016/j.apenergy.2022.120345.

[26] P. Hall, A. Klerkx, and A. Rose, “Blockchain applications in agriculture: Opportunities and challenges,” Precis. Agric., vol. 25, no. 2, pp. 201–220, 2024, doi: 10.1007/s11119-023-10012-8.

[27] . Adams and S. Smith, “Ethical implications of AI in blockchain systems: Bias, fairness, and accountability,” Ethics Inf. Technol., vol. 23, pp. 411–423, 2021, doi: 10.1007/s10676-021-09584-9.

[28] R. Clark, D. Richards, and P. K. Yu, “Regulatory frameworks for AI and blockchain: A comparative analysis,” Law Policy, vol. 44, no. 3, pp. 225–246, 2022, doi: 10.1111/lapo.12212.

[29] K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” Information and Software Technology, vol. 64, pp. 1-18, 2015, doi: 10.1016/j.infsof.2015.03.007

[30] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, Mar. 2021, [online] Available at: https://www.prisma-statement.org/