In response to the increasing demand for sustainable energy solutions, infrastructures are turning to diverse storage mechanisms to harness renewable sources, particularly hydrogen in fuel cells. Proton Exchange Membrane Fuel Cells (PEMFCs) have emerged as a popular technology due to their efficiency and environmental benefits, being widely utilized in electric vehicles and energy-efficient systems. Maximizing the power output of fuel cells requires effective tracking of the Maximum Power Point (MPP), leading to the development and enhancement of Maximum Power Point Tracking (MPPT) strategies like the Perturb and Observe (P&O) method. Recent research focuses on improving MPPT techniques to make systems more efficient by accelerating the reach of the MPP and enhancing controller accuracy. Meta-heuristic optimization methods, which mimic biological and natural processes, are being explored to optimize fuel cell efficiency, with the potential to conduct global searches and adapt to changing circumstances. Various studies have combined meta-heuristic algorithms with conventional MPPT techniques to improve the robustness, convergence speed, and adaptability of fuel cell systems. Ensemble learning methods and the integration of hybrid optimization approaches are also gaining traction in enhancing the overall performance of fuel cells. These advancements hold promise for improving energy extraction efficiency, ensuring system stability, and mitigating environmental variations in the quest for sustainable energy solutions.