The article discusses the significance of renewable energy sources (RESs) in mitigating environmental issues caused by the reliance on fossil fuels. It particularly focuses on Proton Exchange Membrane Fuel Cells (PEMFCs) due to their simple structure, high energy density, and adaptability for different applications. The complexity of accurately modeling and optimizing PEMFCs is highlighted, necessitating precise mathematical models for predicting performance under diverse scenarios. Various research efforts have been detailed, including optimization methodologies like Gorilla Troops Optimizer, fuzzy logic based reasoning, Grasshopper Optimization Algorithms, and evolutionary strategies. These studies aim to identify optimal parameters for PEMFC systems to enhance efficiency and durability. Furthermore, the article reviews the challenges in computational modeling for PEMFCs and proposed solutions that balance accuracy and computational efficiency. Hybrid models utilizing deterministic and stochastic methods, deep learning techniques, genetic algorithms, and machine learning have been explored to optimize PEMFC performance. The sentiment of the article is positive, emphasizing the advancements made in optimizing PEMFCs through various research methodologies. The importance of balancing accuracy, computational efficiency, and scalability in modeling and parameter identification for improving PEMFC efficiency and durability is a key takeaway from the comprehensive review of research efforts.