The article focuses on the optimization of proton exchange membrane fuel cell (PEMFC) models to improve efficiency and performance in renewable energy applications. It introduces a novel optimization method called Parrot Optimizer (PO), inspired by parrots' behavior, to accurately estimate seven unknown parameters in the PEMFC electrochemical model. The article highlights the importance of PEMFCs in the transition to sustainable energy sources due to their high efficiency, low operating temperature, and durability. The Parrot Optimizer method involves two key enhancements: Opposition-based Learning to boost search efficiency and a Local Escaping Operator to enhance exploration capabilities and prevent getting trapped in local optima. The article validates the Parrot Optimizer through the application of benchmark functions and empirical demonstrations on three distinct PEMFC stacks, showing improved optimization results compared to standard methods. Furthermore, the article discusses the significance of PEMFCs in various applications, emphasizing their solid-state electrolyte, low operating temperature, compact design, and environmental friendliness. It explains the role of PEMFCs in converting chemical energy into electrical energy using hydrogen and oxygen as fuel. The article also touches on the challenges in modeling PEMFCs, such as accurately estimating unknown parameters and the need for advanced optimization techniques. It compares data-driven modeling techniques with equivalent circuit models, highlighting the importance of accurate parameter estimation for reliable PEMFC modeling. Overall, the article provides insights into the advancements in optimizing PEMFC models using the Parrot Optimizer method, showcasing improved efficiency, performance, and reliability in renewable energy applications.