This study explores the effectiveness of the traditional Firefly algorithm (FA) in optimizing the Gaussian Kernel-based Fuzzy C-means clustering (GKFCM) algorithm by adjusting ‘sigma’ and ‘m’. We compare GKFCM with FA optimization (With FA) to without it (Without FA) using the Calinski Harabasz (CH) index and the number of iterations. For all four datasets analyzed in this study, the findings consistently indicate that the GKFCM algorithm optimized with the Firefly algorithm (FA) performs substantially better than its non-optimized counterpart, achieving higher Calinski Harabasz (CH) scores and requiring fewer iterations across various data types. Results from two initial particle distribution styles confirm FA’s robustness in refining clustering outcomes and emphasize its role in enhancing clustering quality and efficiency.