This study proposes an advanced approach for optimizing and estimating the unknown parameters of proton exchange membrane fuel cells (PEMFCs) using the recently developed Rüppell’s Fox Optimizer (RFO). The RFO is applied to minimize the sum of squared voltage errors between experimental and modeled data obtained under various operating conditions, subject to realistic system constraints. The algorithm’s performance is validated using three widely recognized PEMFC benchmark cases: the Ballard Mark V, Horizon H-12 Stack, and Temasek 1 kW unit. These cases encompass different operating conditions, including variations in cell temperature and the partial pressures of hydrogen and oxygen, ensuring a comprehensive evaluation of the method’s robustness and adaptability. Comparative analyses are conducted against recently published optimization techniques to assess the reliability, convergence behavior, and estimation accuracy of the RFO. Statistical performance indicators further substantiate the algorithm’s capability in achieving precise parameter identification. Results demonstrate that the RFO offers a promising and efficient alternative for nonlinear, multi-parameter PEMFC modeling, providing accurate representations of experimental data and stable convergence across diverse test scenarios.