Modern AI-Based Optimization Strategies for Secure and Green Blockchain Systems: A Systematic Study

Abstract

The growing complexity and energy demands of Blockchain systems have intensified the need for intelligent and sustainable optimization strategies. This paper conducts a PRISMA-based systematic review of modern Artificial Intelligence approaches designed to enhance Blockchain security and energy efficiency. By analyzing recent advancements such as multi-agent reinforcement learning and graph neural networks, the study identifies how AI contributes to securing consensus mechanisms, detecting anomalies, and reducing energy consumption. Unlike prior reviews, this work provides an integrated perspective that connects AI-driven optimization with both security resilience and environmental sustainability. The findings highlight current research gaps, particularly the absence of large-scale experimental validation, and outline future directions toward intelligent, adaptive, and energy-aware Blockchain systems.