To artificial intelligence (AI), oncology is a revolutionary topic, especially in the field of risk evaluation and disease monitoring of breast cancer. Deep learning and machine learning models are two AI control methods that demonstrate very high accuracy in early detection and personalized risk estimation. AI augments the ability to predict the risk of breast cancer and incorporate clinical, genetic, and imaging information to monitor the course of the disease. The use of noninvasive liquid biopsies and advanced imaging methods significantly enhances AI capabilities in real-time tumor analysis and planning. Clinical methods despite their commitment involve data distortion, a lack of standardization, ethical issues, and regulatory constraints. The accuracy of AI models is often undermined by disruptions in training data records, which also limits how widely they can be utilized. In addition, there remain numerous ethical issues regarding the privacy of medical information and the transparency of AI. In order for AI use in cancer to be equitable and effective, these limitations need to be mitigated. Federated learning permits distributed model training without compromising on patient privacy, but explanatory abilities enhance transparency and trust by giving good insight into AI control decisions. These advances can make a huge impact on the accuracy, justice, and clinical acceptability of AI systems in the therapy of breast cancer. Physician practitioners can apply AI potential to enhance patient outcomes and worldwide incidence of breast cancer by incorporating in regular clinical procedures. AI can revolutionize precision medicine by enhancing the efficacy and availability of breast cancer identification, treatment, and monitoring through constant research and careful application.