Breast cancer ranks among the leading dangerous conditions that affect millions of women throughout the world. The three key elements necessary to enhance breast cancer survival rates include early detection followed by accurate diagnosis, and prompt treatment. Modern healthcare developments through Artificial Intelligence (AI) have introduced substantial benefits for breast cancer therapy during the last several years. The analysis evaluates the role of AI technology for breast cancer diagnosis, as well as treatment strategy planning and patient surveillance tasks. The analysis of images using machine learning and deep learning AI methods like Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), Random Forest (RF) applications, and Extreme Gradient Boosting (XGBoost) produces high analysis accuracy for mammography and ultrasound tests and histopathology examinations. AI uses large amounts of data in order to forecast and individualize treatment. It enhances accuracy in surgical, immunotherapy, radiation and chemotherapy and limits the side effects. Other innovations used in advancing personalized medicine include the idea of nanoradiogenomics. But there are still hurdles, such as validation in clinical settings and data security, and explainable AI. AI processes big data to make predictions, but its effective utilization and performance should be guided by ethical principles and incorporated in clinical workflows to ensure responsible use in healthcare. The chapter also demonstrates how AI works as a supportive tool for healthcare providers instead of replacing their medical skills by enhancing clinical workflow performance, along with diagnostic precision and healthcare quality in breast cancer care.