The proton exchange membrane fuel cell (PEMFC) is a potential source of renewable energy that offers a dual benefit of reducing environmental pollution and enabling easy electricity savings. The mathematical model of PEMFC involves several unknown parameters that need to be precisely estimated for developing an accurate model. This process of estimating parameters is known as the parameter estimation of PEMFC and is considered an optimization problem. Although the problem of parameter estimation for PEMFC belongs to the category of optimization problems, it cannot be solved by all optimization techniques as it is a complex and nonlinear problem. Therefore, this paper presents a new parameter estimation technique based on adopting a recently published metaheuristic algorithm known as the artificial hummingbird algorithm (AHA). AHA is simple and easy to implement as its main advantages encourage us to adopt it for tackling this problem. However, unfortunately, AHA suffers from slow convergence speed and hence will consume a huge number of function evaluations even reaching the desired outcomes. Therefore, two improvements have been applied to the classical AHA for proposing a new variant , namely IAHA, for overcoming the parameter estimation of PEMFC stacks. IAHA was applied to estimate the unknown parameters of six different PEMFC stacks and compared with 11 well-known competing optimizers in terms of accuracy of outcomes, convergence speed, stability, and CPU time. Based on the experimental results, IAHA outperforms all other algorithms across all performance parameters except for CPU time, which is on par with the other methods.