Histopathology image’s Nuclei Segmentation is a vital stage in computer-assisted automated analysis of various serious disorders, including cancer. Fuzzy C-Means (FCM) is a widely employed partitional clustering method that has been effectively applied for image segmentation. Nonetheless, FCM exhibits many limitations, including longer computational time, bad convergence, and susceptibility to noise. This study introduces an enhanced variation of FCM that incorporates two distance metrics, Morphological Reconstruction based input image filtering, a superpixel approach, and membership scaling and filtering for nucleus segmentation. The suggested technique has been evaluated against other fuzzy clustering techniques both visually and quantitatively. Experimental results indicate that the suggested strategy yields improved outcomes compared to other examined methods for segmentation accuracy, computing efficiency, and noise tolerance.