The global demand for clean and efficient energy sources has led to a significant focus on renewable technologies, with Proton Exchange Membrane Fuel Cells (PEMFCs) emerging as a promising solution. PEMFCs offer high energy conversion efficiency, low emissions, and noiseless operation, aligning well with sustainability goals. However, optimizing the performance of PEMFCs poses challenges due to complex internal reactions and the need for accurate parameter estimation. The article delves into various advanced optimization techniques developed for PEMFC parameter estimation. Researchers have introduced algorithms such as Converged Moth Search Algorithm (CMSA), Adaptive Network Based Fuzzy Inference System (ANFIS), and extremum seeking methods to tackle the nonlinearity and coupling within PEMFC systems. These techniques aim to improve accuracy and convergence speed in modeling PEMFC behavior under different operational conditions. Numerous studies have been conducted to enhance the performance of PEMFCs. From the Black Widow Optimization (BWO) algorithm to the Dynamic Ant Colony Optimization (DACO) algorithm, researchers have focused on developing efficient algorithms for precise parameter estimation. Thermal management, fault diagnosis, and power electronic systems have also been explored to optimize the efficiency and reliability of PEMFCs. The field continues to see advancements in algorithm development and modeling techniques to overcome the challenges associated with PEMFC parameter estimation. With a positive sentiment towards the potential of PEMFCs in clean energy systems, researchers are dedicated to refining these technologies for a sustainable energy future.