one of the significant challenges in simulating combustion processes is the precise determination of the Arrhenius kinetic coefficients, as these parameters directly influence the accuracy of reaction rate predictions. In this study, four metaheuristic algorithms—including the Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), a hybrid Genetic–Particle Swarm Optimization algorithm, and the Whale Optimization Algorithm (WOA)—were employed to optimize the coefficients of the Arrhenius equation. To evaluate the performance of these algorithms, the reduction of a two-step methane combustion mechanism into a single-step mechanism was selected as a case study. The novelty of this research lies in the comparative and simultaneous assessment of the aforementioned algorithms, determining the most suitable method based on two criteria: Root Mean Square Error (RMSE) and computational runtime. The results indicated that the Whale Optimization Algorithm provided the most accurate performance in determining Arrhenius coefficients, with an RMSE of 0.03325, whereas the Grey Wolf Optimizer exhibited the fastest performance with a runtime of 1.89 seconds. The findings suggest that the proposed methodology is not only applicable to the reduction of methane combustion mechanisms but also possesses the potential for determining Arrhenius coefficients in other chemical reactions.