As the photovoltaic (PV) market share continues to increase, accurate PV modeling will have a massive impact on the future energy landscape. Therefore, it is imperative to convert difficult-to-understand PV systems into understandable mathematical models through equivalent PV models. However, the multi-peaked, non-linear, and strongly coupled characteristics of PV models make it challenging to extract accurate parameters of PV models. Metaheuristics can address these challenges effectively regardless of gradients and function forms, and have gained increasing attention in solving this issue. This review surveys different metaheuristics to the PV model parameter extraction and explains multiple algorithms’ behavior. Some frequently used performance indicators to measure the effectiveness, robustness, accuracy, competitiveness, and resources consumed are tabulated and compared, and then the merits and demerits of different algorithms are outlined. The patterns of variation in the results extracted from different external environments were analyzed, and the corresponding literature was summarized. Then, challenges for both metaheuristics and application scenarios are analyzed. Finally, corresponding perspectives on future research are summarized as a valid reference for technological advances in PV model parameter extraction.