Fuel cells, particularly Proton Exchange Membrane Fuel Cells (PEMFCs), are gaining recognition for their efficiency and minimal emissions, playing a crucial role in the transition towards cleaner energy sources. Researchers are focusing on precise modeling of PEMFCs to enhance design strategies for a sustainable future, especially in reducing reliance on fossil fuels, pollution mitigation, and ensuring energy security. Various optimization techniques, including traditional and advanced methods such as metaheuristics, neural networks, and bio-inspired algorithms, are being employed to model PEMFC parameters accurately. The research emphasizes the significance of modeling PEMFC performance for micro-grids and smart grid applications. Challenges include degradation modeling, parameter estimation, and accurately predicting lifespan. Studies are introducing novel algorithms like the lightning search algorithm and Combined Owl Search Algorithm to optimize PEMFC parameters, improve accuracy, and reduce errors. The advancements in PEMFC modeling address the complexities of clean energy technologies, paving the way for a sustainable and cleaner energy future.