Cardiotoxicity is a common complication during the cancer treatment of breast cancer patients, increasing their physical and mental suffering and even endangering their lives. Therefore, early identification of high-risk patients and taking targeted preventive measures is crucial. Risk prediction models for cardiotoxicity related to breast cancer treatment are important methods for assessing high-risk patients and have been extensively studied by scholars both domestically and internationally. The paper categorizes existing models into specific and general models based on the treatment methods received by the study population. It provides a detailed explanation and comparative analysis of the construction processes, methods, predictive factors, validation of effectiveness, and applications of these models. The analysis shows that the construction methods of existing models mainly use traditional statistical methods such as Logistic regression and Cox proportional regression, with a few using machine learning algorithms. The predictive factors are mostly age, hypertension, diabetes, and BMI, with good predictive performance. However, there are significant differences in the predictive factors among the models, and there is an imbalance between development and application. Future research should focus on clinical application and localization verification of models, combine artificial intelligence technology, enrich new modeling methods, and conduct multi-center, large-sample prospective studies to improve model stability. This will provide effective screening tools for clinical healthcare professionals to identify cardiotoxicity. This paper can serve as a reference for the construction and application of risk prediction models for cardiotoxicity related to breast cancer treatment by clinical healthcare professionals in China.