Objective：To establish and validate a prediction model combined machine learning with radiomics features in predicting outcome after mechanical thrombectomy in acute stroke. Methods：Imaging data of acute stroke patients in Nanjing First Hospital were retrospectively collected. These patients were divided into a training set(n=105)and a test set(n=50)according to random number table method. Acute stroke(n=45)in the Second People’s Hospital of Changzhou were collected as the validation set. A.K. software was used to extract radiomics features on diffusion weighted imaging(DWI)and perfusion weighted imaging(PWI). Least absolute shrinkage and selection operator(LASSO)regression model was used to screen the features，and then，the selected features were used to establish the prediction model by support vector machine(SVM)classifier. Receiver operating characteristic(ROC)curve was used to evaluate the predictive efficacy of the model，and the validation set was used to verify the generalization ability of the model. Results： One thousand three hundred and sixteen radiomics features of each patient were extracted from DWI and PWI，and 40 features highly related to outcome after mechanical thrombectomy in acute stroke were screened after dimension reduction. ROC analysis showed that the area under curve(AUC)of DWI+PWI model(training set：0.981；test set：0.891)was higher than those of DWI or PWI model，and the accuracy were 0.943 and 0.900，respectively. The results of validation of the model showed that the prediction model based onDWI + PWI was also better than that of single sequence(DWI or PWI)，the sensitivity and specificity were 0.864 and 0.783 respectively，and the accuracy was 0.822. Conclusion：The prediction model combined machine learning and radiomics can effectively predict outcome after mechanical thrombectomy in acute stroke，and has good generalization ability.