O. Apata, Josiah Lange Munda, Emmanuel Migabo
Artificial intelligence (AI) has become integral to predictive maintenance (PdM) in renewable energy systems (RES), enabling the detection of faults, forecasting of degradation, and optimization of performance. However, existing reviews are fragmented, focusing either on specific energy domains or algorithmic families without a unified framework that connects AI methods to real-world deployment. This paper presents a novel, cross-domain synthesis for solar, wind, hydro, and hybrid systems. Its originality lies in a dual-axis classification framework that maps AI models to their functional roles while accounting for the data realities of different energy infrastructures. Unlike prior studies, this review integrates data characteristics into the comparative analysis, revealing how data constraints shape model selection, scalability, and reliability. By bridging methodological rigor with operational feasibility, this paper establishes a foundation for adaptive, transparent, and scalable AI integration in RES. The findings offer actionable insights for researchers, engineers, and policymakers seeking to advance intelligent asset management in the context of global energy transition.