A recent study introduces the Improved Parrot Optimizer (IPO) as a novel approach to significantly improve parameter estimation for proton exchange membrane fuel cells (PEMFCs). This innovative optimization method addresses the challenges in accurately estimating parameters crucial for modeling and predicting performance in PEMFC systems. By integrating Opposition-Based Learning and Local Escaping Operator, the IPO enhances traditional optimization techniques, enabling more effective exploration of potential solutions and avoiding local optima. Empirical testing on various PEMFC stacks demonstrates the IPO's capability in achieving substantially improved results, reducing the sum of squared errors and improving efficiency compared to previous methods. The study highlights the importance of accurate parameter estimation in enhancing the overall effectiveness of PEMFC applications and renewable energy technologies. The research outcomes suggest potential applications of the IPO's methodologies beyond PEMFC systems, indicating opportunities for optimizing other energy technologies. Feedback from sensitivity analysis underscores the significance of precise parameter estimation for industries relying on efficient energy conversion. Overall, the IPO presents promising advancements in parameter optimization for PEMFCs and renewable energy systems, laying the foundation for more efficient and sustainable energy technologies.