In the contemporary landscape of machine learning, medical image analysis has experienced monumental leaps forward. Spearheading this progression are cutting-edge clustering and segmentation methods having reshaping our analytical capabilities. This piece delves into the prowess of the Fuzzy C-Means (FCM) clustering technique: a machine learning-centric strategy. By scrutinizing five diverse case studies, we unravel the tangible benefits and the expansive potential of FCM. To ensure a comprehensive view, we also navigate through other prominent image segmentation methodologies, including Thresholding, Watershed, and K-means clustering. To evaluate the resultant images from these methods, we have adeptly employed the fuzzy inference system (FIS). Our analytical juxtaposition underscores FCM’s distinctive edge over other techniques, demonstrating its finesse in producing intricate and superior outcomes. Reinforcing the marriage of advanced technology with in-depth research, all our examinations and simulations were seamlessly executed by utilizing MATLAB’s robust arsenal.