The quest for clean and sustainable energy sources has propelled the development of fuel cells as a promising option due to their efficiency and low emissions. Proton Exchange Membrane Fuel Cells (PEMFCs) are particularly suitable for various applications, from residential to industrial, suggesting a pivotal role in a cleaner future. Different modeling methods, including analytical, empirical, and theoretical approaches, are utilized to enhance PEMFC performance. Recent advances in Reinforcement Learning (RL) methods, specifically Actor-Critic algorithms, have shown significant potential in optimizing fuel cells by predicting complex system behaviors. RL's adaptability to changing conditions and its ability to learn from data make it a promising tool for the energy sector, with Actor-Critic algorithms offering efficient learning in complex, continuous state-action spaces. The combination of Proximal Policy Optimization (PPO) with the REINFORCE update rule in RL models has demonstrated superior performance in fuel cell optimization, outperforming traditional methodologies. The use of Actor-Critic RL methods in optimizing PEMFCs showcases a positive sentiment towards leveraging advanced technologies for sustainable energy solutions.