To develop and validate a clinical-radiomics nomogram for predicting early ischemic stroke risk in patients with transient ischemic attacks (TIA). A retrospective training dataset (n=76) and a prospective validation dataset (n=34) of patients with TIA were studied. Image processing was performed using ITK-snap and Artificial Intelligent Kit. Radiomics features selection were done in R. A nomogram predicting recurrent TIA/stroke in 90 days as a recurrent ischemic event was established. The performance of the models was assessed by computing the receiver-operating characteristic and decision curve analysis (DCA). High proportion of diabetes and hypertension in the recurrent than those in the stable patients in both training and validation dataset (P<0.05). Recurrent patients had significant higher ABCD2 score and plaque score than stable patients. ABCD2 score and necrotic/lipid core area were independent risk factors for recurrent ischemic events (OR=2.75, 95% CI: 1.47-6.40; and OR=1.20, 95% CI: 1.07-1.41). The radiomics model had AUC value of 0.737 (95% CI: 0.715-0.878) in training dataset and 0.899 (95% CI: 0.706-0.936) in the validation dataset, which was superior to the ABCD2 score and plaque model for predicting stroke recurrence (P<0.05). The nomogram predicting recurrent ischemic events was 0.923 (95% CI: 0.895-0.978) in the training dataset and 0.935 (95% CI: 0.830-0.959) in the validation dataset. DCA confirmed the clinical value of this nomogram. The nomogram, based on clinical ABCD2 score, carotid plaque components and radiomics score, shows good performance in predicting the risk of recurrent ischemic events in TIA.