Objective This study aimed to explore the ability of ultrasonographic radiomics in predicting the occurrence of in-stent restenosis （ISR） after carotid artery stenting （CAS） by analyzing the correlation between radiomic features of responsible plaques in carotid artery stenosis and the incidence of ISR. Methods A retrospective collection was conducted on 206 cases that underwent CAS treatment at our hospital. The enrolled patients were randomly split into a training set （144 cases） and a test set （62 cases） at a 7：3 ratio. We utilized the Darwin Intelligent Research Platform to extract radiomic features from each region of interest， and then screened 1125 ultrasonographic radiomic features. Different machine learning algorithms were employed to construct diagnostic models， and the best-performing classifier was selected. Various prediction models were established， including a clinical-ultrasonographic feature model， a radiomic model， and a combined clinical-ultrasonographic-radiomic model. Results Multivariate logistic regression analysis in the training set revealed that hypertension， hyperuricemia， triglycerides， and plaque location were independent risk factors for ISR after CAS. For the clinical-ultrasonographic model， the area under the curve （AUC） values for the training and validation sets were 0.896 and 0.644， respectively. The corresponding AUC values for the radiomic model were 0.961 and 0.715， while those for the combined model were 0.947 and 0.727. Conclusion The radiomic model demonstrates superior performance in predicting ISR compared to the traditional clinical-ultrasonographic model. The combined model exhibited an enhanced ability to predict ISR occurrence， thereby improving the diagnostic performance of traditional assessments。