Amid escalating climate challenges and the inefficiencies of linear economic models, this review examines how artificial intelligence (AI) can enable circular economy (CE) strategies to advance environmental sustainability in the energy sector. Drawing on 40 peer-reviewed studies from 2020 to 2025, the research highlights AI applications like machine learning, predictive analytics, and digital twins which optimize resource use, reduce waste, and enhance energy efficiency across industrial and energy systems. AI demonstrates significant potential in integrating renewables, extending battery life, optimizing smart grids, and transforming market operations through automated trading and blockchain-enabled transparency. However, barriers like high costs, data accessibility issues, regulatory gaps, and skills shortages hinder widespread adoption, particularly among small and medium enterprises. Emerging solutions, including federated learning for decentralized energy management and simulationbased design for material efficiency, offer promising pathways forward. The study emphasizes the need for targeted policies, open data frameworks, and interdisciplinary collaboration to address systemic challenges. By providing a comprehensive review that aims to accelerate the integration of CE principles into the energy sector through AI, while calling for further empirical research to evaluate long-term sustainability outcomes and ethical implications.