An Iterative Systematic Analytical Review of Machine Learning Techniques for Blockchain Optimization

Abstract

The exponential growth of blockchain and machine learning (ML) technologies has catalyzed innovations across domains; however, the lack of comprehensive reviews addressing their integration limits our understanding of their synergistic potential. Existing reviews focus on specific applications and neglect scalability, security, and performance metrics that are critical for deploying ML-blockchain frameworks in complex environments. To bridge these gaps, this study conducts a comprehensive review of the most recent state-of-the-art research that evaluates methodologies, performance metrics, and their applicability. The reviewed methods include random forests, federated learning (FL), explainable AI (XAI), reinforcement learning (RL), and federated/hybrid models such as federated reinforcement learning and learning chains. These methods have emerged with the potential to support the balance among accuracy, privacy, and scalability across a variety of domains, such as the Internet of Things (IoT), healthcare, smart grids, and decentralized finance scenarios. This review identifies optimal methods for blockchain optimization and scalability enhancement, while providing a roadmap for integrating advanced ML techniques with blockchains. The findings will assist in significantly advancing domain knowledge in blockchain and guide future research and real-world implementation operations.