As the world grapples with environmental degradation and the energy crisis, the focus on renewable energy sources has intensified. Hydrogen, with its high specific energy density and clean emission profile, is emerging as a key player in the transition to sustainable energy. Particularly, Proton Exchange Membrane Fuel Cells (PEMFCs) have garnered attention for applications in transportation and stationary power generation. Major automotive companies like Honda and Toyota are investing in fuel-cell electric vehicles, showcasing the growing interest in hydrogen fuel technology. Modeling PEMFC systems poses challenges due to their complex nature, but advancements in mathematical modeling have improved our understanding of their behavior. Studies have delved into various aspects of PEMFCs, including materials, system integration, degradation, and operational strategies. Machine learning algorithms have shown promise in modeling PEMFCs accurately and efficiently, with researchers employing techniques like artificial neural networks and support vector machines to optimize system performance. High-temperature PEMFCs, in particular, offer advantages in waste heat recovery and simplified fuel processing. By integrating deep learning with multiphysics simulation, researchers aim to create hybrid models for optimizing fuel cells effectively. This holistic approach to PEMFC optimization and modeling signifies a significant step towards a sustainable and efficient energy future.