The aim of this study is to utilize machine learning models for the prediction of the abrasion resistance of pervious concrete. 150 sets of pervious concrete abrasion resistance test data were collected and a database was constructed. 6 input parameters were identified using feature correlation analysis, namely maximum aggregate size, water/binder ratio, sand ratio, aggregate/binder ratio, fly ash ratio and rotation circle. A variety of machine learning algorithms (XGBoost, Gradient Boosting, AdaBoost, Decision Tree and Random Forest) were used to establish prediction models for the abrasion ratio of pervious concrete, and the model performance was characterized by coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE). The results show that the Gradient Boosting model exhibits high accuracy and small prediction error on both the training and test sets, and the comparative analysis with the existing theoretical models confirms the advantages of the Gradient Boosting model in predicting the abrasion ratio of pervious concrete. The research results can provide a reference for the design and application of pervious concrete, and are expected to reduce the maintenance cost of related projects.