Objective To develop a bimodal radiomics model integrating gray scale ultrasound (US), shear wave elastography (SWE), and clinicopathological characteristics for axillary lymph node (ALN) metastasis prediction in breast cancer. Methods A retrospective analysis was performed on 306 patients who underwent breast cancer surgery in Nanjing Drum Tower Hospital from June 2018 to December 2021.They were divided into a training cohort (n = 214) and a test cohort (n = 92) randomly in a ratio of 7:3. Region of interest (ROI) segmentation and feature extraction were performed based on preoperative US and SWE, respectively. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key features and construct radiomic biomarkers (RIS) of US and SWE, respectively. Univariate and multivariate logistics regression were utilized to construct the radiomics model based on clinicopathological data and RIS. The area under the receiver operating characteristic curve (AUC) was used to evaluate the incremental value of the bimodal radiomic model than the two single-mode models and the sonographer. Results Based on 13 US features and 17 SWE features screened by LASSO, the RIS of US and SWE were constructed. Combined with BI-RADS grading, tumor classification, US-RIS and SWE-RIS, the dual-mode logistics prediction model was constructed. For the training and test set, the AUC, accuracy, sensitivity and specificity of the model were 0.926, 84%, 78%, 88%, and 0.863, 78%, 62%, 88%, respectively. The performance of the dual-mode model was better than those of the single mode US model, the single mode SWE model and the sonographers（P<0.05）. Conclusions A dual-mode radiomics model combined with US and SWE images and clinicopathological information can identify the risk of axillary lymph node metastasis of breast cancer. It can provide a non-invasive method to guide individualized treatment preoperatively, and to avoid excessive sentinel lymph node biopsy.