This paper presents a comprehensive review of artificial intelligence-based maximum power point tracking (MPPT) techniques for photovoltaic systems. The study analyzes conventional MPPT algorithms and recent AI-driven approaches including neural networks, fuzzy logic, reinforcement learning, and hybrid optimization methods. A systematic taxonomy of MPPT techniques is provided, along with a comparative analysis of tracking efficiency, convergence speed, computational complexity, and robustness under dynamic environmental conditions. Furthermore, practical implementation challenges for embedded and real-time photovoltaic systems are discussed. Finally, the paper proposes a future research roadmap highlighting emerging directions such as lightweight AI models, edge-AI deployment, and adaptive hybrid MPPT strategies for next-generation photovoltaic energy systems.
SAFA BAZRAFSHAN (2026) studied this question.