Abstract: Radiogenomics is a high-throughput research method that correlates genomic data with imaging features, and is now applied widely to the identification of molecular subtypes of breast cancer and the assessment of cancer risk. Radiogenomics, based on machine learning and big data technologies, has shown tremendous potential in personalized diagnosis and treatment of breast cancer. To summarize the current research status and future prospects of machine learning technology in breast cancer radiogenomics, the genetic characteristics of breast cancer and the methods for obtaining breast cancer imaging data were first introduced, and the application of machine learning technology in predicting the benign/malignant nature of breast cancer was analyzed. Subsequently, deep learning methods applied to breast cancer image segmentation problems were compared and breast cancer radiogenomics models were analyzed. Finally, the current limitations of research and further research directions in breast cancer radiogenomics were pointed out.