ObjectiveTo systematically evaluate the risk prediction models of acute kidney injury in patients with acute coronary syndrome (ACS) based on machine learning, providing a reference for clinical selection of appropriate risk assessment tools. MethodsClinical studies using machine learning methods for predicting the risk of acute kidney injury in ACS patients were retrieved from PubMed, Cochrane Library, EMbase, Web of Science core database, CNKI, Wanfang Database, Chinese Biomedical Literature Database, and VIP Journal Database. The retrieval time was from the establishment of the database to May 24, 2025, and the quality of the model was evaluated using the prediction model bias risk assessment tool. ResultsNine articles were included, using 20 machine learning methods to construct 58 prediction models. The area under the receiver operating characteristic curve ranged from 0.740 to 0.894. The most commonly used predictors were age and creatinine. The overall bias risk of the included studies was relatively high, but the applicability was good. Conclusion: Machine learning models can identify the risk of acute kidney injury in ACS patients. All models have good predictive potential, but they are still in the development stage. It is recommended that future studies adopt prospective research and external validation to improve the stability and predictive accuracy of the model.