AI-enhanced blockchain technology: A review of advancements and opportunities

This review paper offers a comprehensive survey of the convergence between artificial intelligence and blockchain technology. The author categorizes recent advancements into three main domains: AI-driven optimization of blockchain consensus mechanisms, AI-enhanced smart contract security, and the development of decentralized AI marketplaces. The paper synthesizes literature to explain how machine learning algorithms can predict mining difficulty, detect anomalies in transaction patterns, and optimize shard management in scaling solutions. Furthermore, it addresses the technical challenges of reconciling AI's computational requirements with the resource-constrained environment of blockchain nodes. The research findings indicate that AI can significantly reduce the energy consumption of Proof-of-Work systems through predictive resource allocation. The paper concludes by highlighting future research trajectories, specifically focusing on privacy-preserving machine learning and the integration of federated learning models within decentralized networks. It serves as a foundational resource for researchers looking to understand the interplay between autonomous agents and distributed ledgers.