This study focuses on enhancing breast cancer detection by combining advanced image processing techniques with the Fuzzy C-Means (FCM) clustering algorithm. Beginning with a comprehensive review of existing detection methods, the research addresses their limitations and proposes a methodology comprising image preprocessing, FCM-based segmentation, feature extraction, and classification. Key steps include noise reduction and contrast enhancement in image preprocessing, precise lesion segmentation using FCM clustering, and effective characterization through feature extraction. Machine learning algorithms are then employed for lesion classification. Evaluation on diverse datasets demonstrates superior detection accuracy compared to existing methods. The integration of image processing with FCM clustering holds promise for improving breast cancer detection and warrants further refinement and validation for broader clinical use. Here we going to compare the time, Area, Sensitivity, Specification and accuracy of the malignant images and Benign images