Objective To construct a multimodal fusion model integrating clinical features, conventional ultrasound parameters and ultrasomics features for preoperative prediction of axillary lymph node metastasis (ALNM) in breast cancer, and to evaluate its cross-center generalizability and clinical application value. Methods A total of 321 breast cancer patients were retrospectively enrolled from Jan. 1, 2020 to Jan. 1, 2025, including 230 patients from No. 910 Hospital of Joint Logistics Support Force (randomly assigned to training set or internal validation set at a 7∶3 ratio, yielding 161 and 69 cases, respectively) and 91 patients from Quanzhou First Hospital (serving as external validation set). Recursive feature elimination method was adopted to screen clinical and conventional ultrasound parameters, and least absolute shrinkage and selection operator regression was used to select ultrasomics features. Based on the optimal algorithm glmnet, a clinical model (incorporating only clinical and conventional ultrasound parameters), an ultrasomics model (incorporating only ultrasomics features), and a fusion model (incorporating clinical and conventional ultrasound parameters together with ultrasomics features) were established. The predictive performance and generalizability of each model for ALNM were evaluated in the training set, internal validation set, and external validation set using receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, Brier score, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). In a subgroup of 142 patients who underwent both sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND), the diagnostic efficacy was compared between the fusion model and SLNB. In the external validation set, based on the optimal cut-off value determined by the Youden index of ROC curve and the clinical benefit-to-risk ratio calculated by DCA, the patients were assigned to low-, intermediate-, or high-risk groups to evaluate the ability of the fusion model to guide clinical decision-making. Subgroup analyses stratified by molecular subtype (Luminal, human epidermal growth factor receptor 2-positive, and triple-negative) or clinical T stage were further performed to assess performance of the fusion model. Results Among the 321 patients, 139 (43.3%) had positive ALNM. Four key clinical and conventional ultrasound parameters and 18 ultrasomics features were screened. In the internal and external validation sets, the area under curve (AUC) values of the fusion model were 0.829 (95% confidence interval ［95%CI］ 0.779-0.878) and 0.854 (95%CI 0.810-0.898), respectively, which were superior to those of the clinical and ultrasomics models. The fusion model showed excellent calibration, with Hosmer-Lemeshow test P values of 0.921 and 0.365, and Brier scores of 0.010 and 0.020 in the internal and external validation sets, respectively. DCA demonstrated that the fusion model yielded the highest net benefit across most threshold probability ranges. With the fusion model as the reference, the clinical model and ultrasomics model had negative NRI and IDI values. In the comparison subgroup, the sensitivity of the fusion model for predicting ALNM was 0.862, showing no significant difference compared with SLNB (sensitivity=0.914, McNemar test P=0.286). In the subgroup analyses by molecular subtype or clinical T stage, the fusion model effectively identified patients at high risk for ALNM. Conclusion The multimodal fusion model based on clinical, conventional ultrasound, and ultrasomics features can accurately predict ALNM in breast cancer, with favorable stability, calibration, and cross-center generalizability. This model is expected to provide a reliable non-invasive quantitative basis for preoperative axillary lymph node management strategies, including decision-making regarding SLNB or ALND.