<p>With the growing global demand for clean energy, photovoltaic (PV) systems have become an important part of modern renewable energy infrastructures. However, their nonlinear behavior, sensitivity to irradiance and temperature variations, and complex operating conditions require advanced optimization and control methods. In this context, Marine Predators Algorithm (MPA) has attracted increasing attention due to its flexible search capability and balanced exploration–exploitation structure. This review presents a systematic analysis of MPA-based approaches applied to PV energy systems. A total of 124 studies were examined, considering only works in which PV systems are the main research focus and MPA is used as a core optimization, estimation, control, or energy management tool. The literature is classified into nine application domains: PV parameter estimation, PV modeling techniques, maximum power point tracking (MPPT) studies, MPPT under partial shading, PV control systems, grid-connected PV systems, PV system design and sizing, PV energy management systems, and advanced PV optimization applications. Comparative insights are provided based on performance indicators such as convergence behavior, tracking performance, root mean square error (RMSE), stability, robustness, efficiency, and cost-related metrics. The findings show that standard MPA remains the most widely used approach, while hybrid and modified variants are increasingly proposed to improve accuracy, convergence, and adaptability. The review also highlights that most studies are simulation-based, whereas real-world data and experimental validations remain limited. Overall, this study provides a structured perspective on current trends, practical limitations, and future research directions in MPA-based PV optimization.</p>