Malignancies affecting mammary tissues rank among the most widespread oncological conditions, constituting a significant global health burden. It represents a major health challenge for women worldwide. Despite advancements in uncovering and treatment, it remains the foremost reason behind cancer-related fatalities in women, underscoring a major public health crisis, with millions of new cases diagnosed annually. Early detection of this malignant cancer significantly increases survival rates, making effective diagnostic techniques essential. This paper explores the various types of cancers comes under the mammary carcinoma, take in ductal carcinoma(IDC), lobular carcinoma (ILC), and more including DCIS and LCIS, highlighting their unique characteristics and diagnostic challenges. Emphasize the integration of traditional diagnostic techniques—including X-ray mammography, ultrasound imaging, and MRI scans—which play a pivotal role in the prompt recognition and analysis of abnormalities associated with the disease. — with modern Computational intelligence and autonomous learning algorithms to enhance diagnostic accuracy. The role of convolutional neural networks (CNNs) in automating image analysis and reducing inter-observer variability is particularly emphasized. As we navigate the complexities of mammary carcinoma diagnosis, this review aims to provide insights into current methodologies, challenges, and future research directions, ultimately striving to improve patient outcomes and healthcare delivery.