This study evaluates various distance functions in Gaussian Kernel-based fuzzy C-means clustering across six datasets. Key findings include the superior performance of the Cosine distance, which consistently yielded the lowest Davies-Bouldin scores, notably 0.3823 and 0.5226 for Datasets 5 and 6, and required fewer iterations to converge, with figures as low as 7 and 8. Squared Euclidean distance also showed effectiveness, particularly with fewer iterations needed for convergence, such as 22 and 41 for Datasets 1 and 3. In contrast, Chebyshev and Minkowski distances, requiring up to 119 iterations for Dataset 2, demonstrated lower efficiency. The evaluation process also involved additional clustering quality metrics, including the Silhouette and Calinski-Harabasz indices, though only the Davies-Bouldin results are detailed in this paper. This analysis highlights the importance of choosing appropriate distance metrics to optimize clustering quality and computational efficiency.