Accurate parameters’ identifications of photovoltaic models is essential for precise simulation and analysis of integrated and standalone photovoltaic systems which is directly influencing performance assessments. Accordingly, this study investigates the procedures of the hippopotamus optimizer for optimal parameters’ identifications of photovoltaic single and double-diode models, as well as the Sandia photovoltaic array performance model. The single and double-diode models simulate the steady-state I-V and P-V principal curves, while the Sandia model predicts maximum power points under various environmental conditions. Reducing root mean quadratic error is adapted as the optimization objective, subjected to operational and design viable constraints. The hippopotamus optimizer’s performance is tested on eight commercial photovoltaic units with diverse technologies, including silicon, poly-crystalline, mono-crystalline, cadmium telluride, copper indium gallium selenide, and amorphous silicon/microcrystalline silicon cells. Thru extensive simulations and comparisons with other optimizers in the literature, the hippopotamus optimizer shows its effectiveness in achieving lowest values of the root mean quadratic errors, indicating a high correlation among modeled and actual dataset points. For instance, using the single-diode model, the optimizer achieves best root mean quadratic error values of 28.210671 mA, 2.039979 mA, 13.79826 mA, 1.721864 mA, and 0.7728666 mA for Kyocera KC200GT, PhotoWatt PWP201, STP6-120/36, and STM6-40/36 modules and RTC France photovoltaic silicon cell, respectively. These results highlight the optimizer’s potential as a powerful tool for enhancing photovoltaic model accuracy. Consequently, the hippopotamus optimizer contributes to improved performance predictions and design precision in photovoltaic applications.