This article introduces a novel optimization approach to improve the parameter estimation of proton exchange membrane fuel cells (PEMFCs), which are critical for diverse applications but are challenging to model due to their nonlinear behavior. The proposed method, HGS-MPA, enhances the Hunger Games Search (HGS) algorithm by integrating Marine Predator Algorithm (MPA) operators, significantly boosting its exploitation capabilities and convergence rate. The effectiveness of HGSMPA was validated on three commercial PEMFC datasets: 250-W stack, BCS 500-W, and NedStack PS6, using the Sum Squared Error (SSE) as the performance metric. Experimental results highlight that HGS-MPA achieves minimum fitness values of 0.33770, 1.31620, and 0.01174 for the respective datasets, outperforming other state-of-the-art algorithms. These findings underscore the method’s potential for accurate PEMFC parameter estimation, offering enhanced performance and reliability