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Highlights: • A novel algorithm is applied to model the Proton Exchange Membrane Fuel Cell. • Different algorithms are referred to compare with Shark Smell Optimization results. • Respect to the results, proposed method is the best option among the other methods. • Two different practical cases are taken into account; for optimization. • Statistical analysis is done on the proposed method to attest to the reliability. Abstract Proton Exchange Membrane Fuel Cells regarded as promising devices for energy conversion systems. This study aims to introduce an exact modeling scheme for the proton exchange membrane fuel cell, which can imitate and model the electrical, electrochemical features of an actual Proton Exchange Membrane Fuel Cell stack. Most of the developed schemes are experimental, multi-variable, and comprise various non-linear terms which must be assessed precisely to assure a decent estimation. In this study, a novel optimization model is employed to determine the Proton Exchange Membrane fuel cell parameters accurately. The Shark Smell Optimizer simulates the hunting process for a Shark. Hence, Shark Smell Optimizer is a nature-inspired and metaheuristic optimization algorithm depends on the rules of metaheuristic proficiency, exploration and exploitation terms to evade errors in local optimums and to obtain appropriate responses. The introduced Shark Smell Optimizing method is examined on five commercial proton exchange membrane fuel cells stack concerning empirical data. Many types of research and operation examination are executed to verify the effectuality of the proposed scheme. Additionally, for more authentication, Proton Exchange Membrane Fuel Cells modeling with Shark Smell Optimizer obtained data is compared with other optimization algorithm results. A statistical analysis also carried out for proposed method, the obtained show the reliability of Shark Smell Optimizer in comparison to other utilized techniques. [ABSTRACT FROM AUTHOR]