Integration of Artificial Intelligence and Blockchain: A Systematic Review of Applications, Architectures, Security, and Open Challenges

Abstract

The integration of artificial intelligence (AI) and blockchain combines adaptive computation with decentralized trust, cryptographic integrity, programmable transactions, and auditable data exchange. This systematic review synthesizes research on integration architectures, applications, reciprocal benefits, security, privacy, scalability, interoperability, and open challenges. Four recurring integration patterns are examined: AI enhancing blockchain operations, blockchain supporting AI systems, bidirectional AI–blockchain architectures, and decentralized or federated intelligent systems. Applications are considered across healthcare, finance, supply chains, the Internet of Things, cybersecurity, energy, smart cities, and education. The review finds that AI can strengthen anomaly detection, fraud analytics, smart-contract analysis, prediction, and network optimization, while blockchain can strengthen data provenance, identity, model traceability, access control, and collaborative learning. However, combined architectures introduce additional trust boundaries involving models, oracles, smart contracts, data pipelines, and cross-system interfaces. Latency, storage growth, interoperability, privacy leakage, model manipulation, poisoning, and governance remain important barriers. The review therefore argues for selective integration: intensive AI computation and sensitive data should generally remain off-chain or in controlled edge/cloud environments, while blockchain should anchor identities, commitments, permissions, provenance, and auditable events. Future research should prioritize verifiable AI outputs, privacy-preserving decentralized learning, trustworthy oracles, cross-chain interoperability, standardized benchmarks, and governance-aware architectures.