DC microgrids with various DC loads from sources like renewable energy, battery storage, and fuel cells are gaining importance in energy distribution due to their efficiency and stability. The integration of hydrogen and solar energy has led to the development of reliable and environmentally friendly storage systems known as hydrogen energy. Fuel cells, especially PEMFCs, play a crucial role in this setup by converting hydrogen's chemical energy into electricity efficiently. Despite the high costs associated with PEMFCs, their sophisticated advancements make them preferred in various applications. PEMFC modeling is crucial for understanding and optimizing their performance, with a focus on accurate parameter estimation. Metaheuristic algorithms are proving to be effective in tackling the non-linear characteristics of PEMFCs and achieving globally optimal solutions. These algorithms have significantly improved the precision and efficiency of PEMFC modeling, enabling researchers to derive unknown parameters more accurately. Various advanced algorithms like PSO and GA, along with newer algorithms such as the shark smell algorithm and whale optimization algorithm, are being used to estimate PEMFC parameters effectively. The utilization of metaheuristic algorithms represents a positive trend in enhancing the modeling and operational efficiency of PEMFCs, showcasing the continuous advancements in this field.