Purpose: This study aimed to develop and validate a predictive model using radiomics features from automatic breast volume scanner (ABVS) and 2D ultrasound images to preoperatively assess Ki-67 expression in breast cancer (BC), thereby supporting personalized clinical treatment planning. Methods: Data from 426 BC patients who met the inclusion criteria were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed on the clinical ultrasound characteristics to construct a clinical model. Radiomics features were extracted from both the tumor and the sub-regions based on ABVS and 2D images. The silhouette coefficient was used to evaluate clustering performance and determine the optimal number of clusters. Radiomics-based prediction models were developed using four machine learning classifiers: Logistic Regression, ExtraTree, XGBoost, and LightGBM. A combined model was further constructed by integrating radiomics and habitat radiomics features from ABVS and 2D images with relevant clinical factors. Model performance was evaluated using the Receiver Operating Characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: In the validation set, the area under the ROC curve (AUC) values of the radiomics model (Rad Conclusion: The CM