Abstract Background This study investigated the abilities of radiomics and clinical feature models to distinguish kidney stone-associated urinary tract infections (KS-UTIs) using computed tomography. Methods A retrospective analysis was conducted on a single-center dataset comprising computed tomography scans and corresponding clinical information from 461 patients with kidney stones. To delineate the region of interest (ROI) from CT images, MRcorGL was employed. Subsequently, 436 radiomic features were extracted utilizing Pyradiomics. Out of these, 413 features with an intraclass correlation coefficient (ICC) greater than 0.7 were selected for inclusion, followed by dimensionality reduction and further classification model selection. Multiple machine learning (Three types of shallow learning and four types of deep learning) algorithms were employed to construct radiomics and clinical models in this study. The performance evaluation and optimal model selection were conducted using a combination of results from multiple features analyzed via Receiver Operating Characteristic (ROC) curves and the Delong test. Univariate and multivariate logistic regression analyzed clinical and radiomics features to identify significant variables and develop a clinical model. A combined model integrating radiomics and clinical features was established. Model performance was assessed by ROC curve analysis, clinical utility was evaluated through decision curve analysis, and the accuracy of the model was analyzed via calibration curve. Results Multi-Layer Perceptron (MLP) showed higher classification accuracy than other classifiers (AUC for radiomics model: train 0.96, test 0.94; Accuracy_standard deviation (Acc_std) for radiomics model: train 0.06, test 0.02; Accuracy_mean (Acc_mean) for radiomics model: train 0.84, test 0.84; AUC for clinical model: train 0.95, test 0.91; Acc_std for clinical model: train 0.07, test 0.02; Acc_mean for clinical model: train 0.84, test 0.85). The combined radiomics-clinical model performed best (AUC for combined model: train 0.98, test 0.95; Acc_std for clinical model: train 0.02, test 0.01; Acc_mean for clinical model: train 0.90, test 0.92). Decision curve and calibration curve analyses confirmed the model's clinical efficacy and calibration. Conclusions This study showed the effectiveness of combining radiomics and clinical features from CT scans to identify KS-UTIs. A combined model using MLP exhibited strong classification abilities.