Objective To construct a machine learning model for the classification of hepatic echinococcosis (HE) based on ultrasound radiomics. Methods A retrospective review was conducted on ultrasound images of HE patients from 2005 to 2022 in Shiqu County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, and West China Hospital of Sichuan University. This retrospective, large-scale, two-center study aimed at assessing diagnostic accuracy utilized a machine learning (ML) approach based on ultrasound radiomics for HE classification. Images were stratified into training and independent test sets in an 8: 2 ratio according to lesion type, and the model was trained using a 10-fold cross-validation strategy. The ML model construction process included lesion segmentation (manual delineation and segmentation), radiomics feature extraction, feature preprocessing, and the construction of the HE classification ML models using eight classifiers: support vector machine, auto-encoder, linear discriminant analysis, random forest (RF), logistic regression, adaboost, decision tree, and naive Bayes. The diagnostic performance of the ML model was evaluated using receiver operating characteristic (ROC) curve analysis. Results A total of 4,976 HE patients were included, comprising 2,157 males and 2,819 females, with ages ranging from 8 to 95 years (43.4 ± 16.9). Among them, 1,641 were cystic echinococcosis (CE) patients, 2,981 were alveolar echinococcosis (AE) patients, and 354 were mixed-type patients. A total of 23,452 ultrasound images were used for model training, validation, and testing, including 8,557 images of CE and 14,895 images of AE. The RF model demonstrated the best performance in both the cross-validation and independent test sets, with sensitivity, specificity, accuracy, and area under the curve of 0.71, 0.76, 0.73, 0.82, and 0.62, 0.89, 0.76, 0.86, respectively. Conclusion The RF model exhibits high accuracy and robustness, contributing to the improvement of ultrasound diagnostic capabilities for different subtypes of hepatic echinococcosis in endemic areas, thereby reducing the occurrence of misdiagnosis.