A Comprehensive Review of Integrating AI and Blockchain Security: Innovations, Challenges, and Future Directions

Abstract

ABSTRACT As digital transformation accelerates, the demand for secure, decentralized, and intelligent systems continues to rise across industries. Blockchain technology has emerged as a revolutionary tool for ensuring data integrity and transparency, while Artificial Intelligence (AI) offers unparalleled capabilities in enhancing decision‐making, automation, and security. This review explores the intersection of AI and blockchain, focusing on how AI techniques—including machine learning (ML), deep learning (DL), reinforcement learning (RL), and natural language processing (NLP)—can effectively mitigate security vulnerabilities within blockchain ecosystems. By analyzing over 100 peer‐reviewed studies and real‐world applications across finance, healthcare, supply chain management, and smart cities, this paper highlights the strengths and limitations of current AI‐driven solutions for addressing blockchain security challenges. Specifically, it examines AI's role in strengthening consensus mechanisms, detecting anomalies, identifying smart contract vulnerabilities, and preserving data privacy. Additionally, the review identifies emerging trends such as federated learning, quantum‐resistant cryptography, and decentralized AI models. The review concludes by discussing ethical considerations, regulatory challenges, and the necessity for standardized security frameworks to guide the future of AI‐enhanced blockchain security. Ultimately, this review offers actionable insights for building resilient, scalable, and AI‐driven blockchain systems, paving the way for innovation and enhanced security across industries.