Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes, such as subjective interpretation biases and inefficient multi-dimensional data integration. Artificial intelligence (AI), particularly deep learning and machine learning technologies, has emerged as a transformative tool in addressing these issues. Clinically, AI has been widely applied in imaging screening to improve detection rates and reduce reading time, digital pathology for precise tumor typing and gene mutation prediction, treatment decision-support systems to enhance guideline compliance, and drug research and development to accelerate target identification and virtual screening. Despite these achievements, AI implementation faces challenges, such as data standardization issues, limited model generalization, low clinical accessibility, and unclear ethical-legal responsibilities, which require targeted solutions that include national data standards, multi-center training, hierarchical physician training, and explainable AI. Future directions involve multi-modal data integration, human-AI collaborative multidisciplinary team models, and extension to full-cycle health management from prevention-to-rehabilitation. This review provides a systematic overview of the role of AI in breast cancer care, offering insights for clinical practice and scientific research innovation, and supporting the transition toward personalized and intelligent medicine in oncology.