Objective: This study aimed to develop a glioma grading prediction model utilizing multi-sequence MRI hand?crafted radiomics (HCR) and deep transfer learning (DTL) features, and assess its efficacy in preoperative glioma grading prediction. Methods: We utilized image data from 332 patients (HGG: 258, LGG: 74) in the BraTS2019 dataset. A subset of 30 HGG and 8 LGG were randomly selected as test data, while data from the remaining 294 patients were served as training and validation sets. Initial steps involved extracting HCR and DTL features from T1, T2, T1c, and Flair sequences. Seven machine learning (ML) prediction models were then constructed using HCR features, DTL features, and combined DLR features. The predictive accuracy of each model for HGGs and LGGs was assessed to determine the optimal ML model. The SHAP method was subsequently employed to quantify the importance of model features and for attribution analysis. Results: The ML model based on DLR features exhibited the most superior predictive performance.Specifically, when features were filtered using SVM-RFE, the SVM classifier model integrating T2, T1c, and Flair sequences emerged as the best, achieving an AUC of 0.996 (95% CI: 0.991, 1.000) on the validation set, with a Youden index, accuracy, sensitivity, and specificity of 0.920, 0.976, 0.988, and 0.932, respectively. On the test set, the model displayed an AUC of 0.986 and accuracy of 0.921, indicating robust predictive capacity.SHAP analysis highlighted that the Flair sequence had the greatest feature contribution, followed by the T2 and T1c sequences. Conclusion: The DLR model, based on multi-sequence MRI, proves effective in glioma tumor grade prediction. The optimal model is the SVM classifierusing T2, T1c, and Flair sequences post SVM-RFE selection.