Artificial intelligence (AI) and machine learning (ML) are revolutionizing energy research by enabling unprecedented acceleration in materials discovery, process optimization, and system management. This comprehensive review examines the transformative role of AI/ML across energy applications—from perovskite photovoltaics and battery materials to waste valorization and electrical grid optimization. We analyze how supervised, unsupervised, and reinforcement learning paradigms are applied to predict material properties, optimize energy reactions, and enhance renewable integration. The chapter highlights breakthrough generative models that propose novel materials with targeted characteristics and discuss hybrid approaches that integrate physical constraints for improved validity. Despite demonstrating remarkable predictive accuracy (R2 > 0.90 in most cases) and significant efficiency gains (20–35% energy savings), AI/ML deployment faces challenges including data scarcity, model interpretability, and computational costs. We critically assess current limitations and propose multidisciplinary solutions—including physics-informed AI, explainable models, and standardized validation frameworks—to bridge the gap between computational prediction and experimental realization. This synthesis provides researchers and engineers with both a state-of-the-art survey and a practical roadmap for harnessing AI to advance clean energy technologies.