Despite all the advances in medical techniques, breast cancer remains a major public health dilemma, contributing to the maximum cancer-related deaths in women. Advancements in the field of artificial intelligence (AI) technology and its novel applications in healthcare and medicine have shown promise as an improved diagnostic tool with minimal error, risk prediction, public health surveillance, and disease-treatment modeling, allowing the diagnosis and treatment of cancer in its early stages. This has significantly improved the survival rate, accompanied by faster recovery periods. By further integrating AI into the public health system and breast cancer care, we can dramatically improve cancer diagnosis, allowing for its early detection using data prediction tools to analyze possible risk factors, along with potentially improved treatment methods by enabling improved drug discovery and testing using AI tools. The conventional treatment methods of breast cancer like surgery, ovarian ablation, anti-estrogen therapy, radiation, and chemotherapy rely upon the knowledge and skill of the physician, which may be delimited by available resources or personal biasedness. Machine learning and deep learning algorithms-based AI tools overcome these shortcomings, providing risk and treatment predictions ideated for personalized medicine. In this work, we reviewed the current literature to discuss how AI has changed our understanding of breast cancer—its types, major risk factors associated, key diagnostic methods, and commonly used treatment methods—as well as how AI can be integrated to improve public health. The work also discusses the current technical, ethical, and regulatory limitations that need to be addressed in employing AI-integrated cancer theranostics in public health.