To investigate the value of CT-based tumor and peritumoral radiomics in predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading for non-metastatic clear cell renal cell carcinoma (ccRCC). A total of 503 patients with non-metastatic ccRCC were retrospectively enrolled from 7 tertiary hospitals between February 2017 and December 2023. Patients from 4 hospitals within Shandong Province were divided into a training set and an internal validation set in a 7∶3 ratio, while patients from 3 hospitals outside Shandong Province constituted the external validation set. Regions of interest (ROI) were manually delineated slice-by-slice along the tumor margin on contrast-enhanced CT images. Peritumoral regions were obtained by expanding 10 mm outward from the tumor boundary. Key radiomics features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression with five-fold cross-validation to build tumor models and peritumoral models for predicting the WHO/ISUP grading. Univariate logistic regression was used to screen clinical factors associated with the WHO/ISUP grading, which was included to construct a combined predictive model together with tumor and peritumoral features. The predictive performance of the models for WHO/ISUP grading was evaluated using receiver operating characteristic (ROC) curves, and the Youden index and optimal cutoff value were calculated for risk stratification. Calibration curves were used to validate model performance, and decision curve analysis (DCA) was employed to evaluate the clinical value of the models. The median age [(,)] of the 503 patients was 59.0 (52.0, 66.0) years, with 335 males and 168 females. Univariate logistic regression analysis showed there was a statistically significance in age between low and high WHO/ISUP grades (=0.043). The AUC of the tumor model and peritumoral model in the internal validation set were 0.744 (95%: 0.700-0.788) and 0.742 (95%: 0.709-0.774), respectively. In the external validation set, the AUC of the tumor model and peritumoral model were 0.685 (95%: 0.651-0.720) and 0.655 (95%: 0.628-0.683), respectively. The combined model demonstrated the best predictive performance in both internal and external validation sets, with AUC of 0.760 (95%: 0.721-0.800) and 0.706 (95%: 0.675-0.737), respectively. Using the Youden index calculated from the ROC curve from the combined model, the optimal cutoff value was 0.504 for risk stratification, where 190 cases were classified as low-grade and 313 as high-grade, achieving a concordance rate of 0.718 (361/503) with the WHO/ISUP grading. Calibration curve tests indicated good fit for the combined model (internal validation set: =0.932; external validation set: =0.404). DCA showed that the combined model provided favorable clinical net benefit within the threshold probability range of 0.2-0.8. The combined model incorporating age, CT-based tumor features, and peritumoral features demonstrates good performance in predicting the WHO/ISUP grading for patients with non-metastatic ccRCC.