In this article, a recently developed bio-inspired based manta rays foraging optimizer (MRFO) is attempted for reliable and accurate extraction of the model uncertain parameters of proton exchange membrane fuel cells (PEMFCs). The parameter estimation is formulated as a non-linear optimization problem subject to set of restrictions. The great development and tremendous revolution of computation heuristic-based algorithms are the impetus of the authors to apply the MRFO to solve this constrained optimization problem resulting in a precise PEMFC model. Three case studies of typical field PEMFC stacks namely Ballard type Mark V, NedStack type PS6, and Horizon type H-12. Various I to V datasets are demonstrated to appraise the performance of MRFO among other recent optimizers available in the literature. To be objective and for sake of quantifications, the best scores of minimum fitness values are 0.8533, 2.1360, and 0.0966 for the later said PEMFC stacks, correspondingly. At a later stage, production of various characteristics under varying operating conditions such as changeable cell temperature and regulating pressures are established using the generated best values of PEMFCs model. Further calculations of statistical indices are performed to validate the robustness of obtained results by the MRFO. Through comprehensive performance assessments, it can be confirmed that MRFO is very promising tool for the effective extraction of PEMFCs' model and suggested to be applied for solving other engineering problems.