Breast cancer begins when abnormal cells in the breast grow uncontrollably, often forming lumps or tumors. It typically originates in milk ducts (ductal carcinoma) or lobules (lobular carcinoma) and can spread to surrounding tissues or distant organs if not detected early. Early detection improves the chances of successful treatment and long-term survival. Advances in medical imaging and computational technologies have revolutionized breast cancer diagnostics. The integration of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has enabled the analysis of large-scale imaging datasets with enhanced accuracy and speed. Convolutional neural networks (CNNs), combined with transfer learning techniques and hybrid architectures, have shown remarkable potential in identifying, classifying, and characterizing breast lesions. These intelligent systems not only assist radiologists in making more informed decisions but also promote equitable access to high-quality diagnostics globally. As research progresses, AI-driven tools continue to play a critical role in improving breast cancer outcomes and supporting healthcare equity.