Accurate parameter estimation is vital for optimizing the performance and design of photovoltaic (PV) systems. While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow conver-gence and difficulty balancing exploration and exploitation. This study introduces a novel hybrid approach, WSO-HO, which integrates the strengths of the War Strategy Optimization (WSO) and Hippopotamus Optimization (HO) algorithms, enhanced by the Newton-Raphson method, to achieve precise parameter estimation for PV models. The effectiveness of the WSO-HO algorithm was rigorously evaluated through intensive testing on three different solar panels, including the RTC France solar cell using the single diode model (SDM) and the double diode model (DDM), over 30 iterations. Comparative analysis highlights the superior performance of WSO-HO against conven-tional algorithms, which often struggle with accurately identifying PV model parameters. These promising results demonstrate the significant potential of this hybrid approach to improve parameter optimization in PV systems, en-abling more precise design and enhanced overall system efficiency. Furthermore, the simulation result the perfor-mance of the WSO-HO algorithm was benchmarked against other algorithms reported in the literature, further vali-dating its robustness and effectiveness.