The quest for sustainable and renewable energy solutions has led to a surge in research on hydrogen energy, particularly focusing on Proton Exchange Membrane Fuel Cells (PEMFCs). These fuel cells are gaining prominence due to their cleanliness, abundance, efficiency, and versatility. Researchers are employing advanced optimization techniques to improve the accuracy and reliability of parameter estimations for PEMFC modeling. Meta-heuristic algorithms inspired by evolutionary biology, physics, and social behavior are being used to tackle the complex non-linear optimization challenges in PEMFC parameter identification. Various methods such as Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution are categorically aiding in the optimization of PEMFC stacks. Recent studies have introduced novel approaches like P systems-based optimizers and biogeography-based optimization with mutation strategies to further enhance the modeling and parameter estimation of fuel cells. The advancements in optimization techniques are crucial for enhancing the efficiency, durability, and applicability of fuel cell systems for sustainable energy production.