<p>This study aimed to develop and validate a clinico-radiomics model integrating radiomics features from multiparametric MRI and clinical scoring systems to predict hemorrhagic transformation (HT) in acute ischemic stroke. A total of 918 patients were retrospectively included. Patients from Institution A who underwent MRIs between 2017 and 2019 were assigned to the training set (<i>n</i> = 792), whereas those from 2020 (<i>n</i> = 78) formed the internal validation set. External validation included 48 patients from Institution B. All patients underwent multiparametric MRI, including diffusion- and perfusion-weighted imaging, fluid-attenuated inversion recovery, and gradient-echo. Radiomics features were selected using the least absolute shrinkage and selection operator regression and random forest models. Clinico-radiomics models were developed using logistic regression by combining radiomics features with clinical scoring systems (HAT, SEDAN, DRAGON, SITS-ICH). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), with DeLong’s test for comparison. Ten radiomics features were selected: time-to-peak, mean transit time, time-to-maximum, relative cerebral blood volume, and relative cerebral blood flow. The radiomics model demonstrated comparable performance to clinical models in the internal validation set (AUC: 0.79 vs. 0.75–0.81); but outperformed them in the external validation set (AUC: 0.85 vs. 0.66–0.68). Clinico-radiomics models demonstrated higher AUCs than radiomics or clinical models in both internal (AUC: 0.81–0.84 vs. 0.79 vs. 0.75–0.81) and external validation sets (AUC: 0.86–0.89 vs. 0.85 vs. 0.66–0.68). These findings suggest that clinico-radiomics models offer improved predictive accuracy for HT compared to radiomics or clinical scoring models alone.</p>