Objective To develop a prognostic model that integrates preoperative imaging， pathological features， and tumor marker indexes for predicting metastasis in sentinel lymph nodes（SLN）. Methods The preoperative examination data of 232 breast cancer patients admitted to the First Affiliated Hospital of Zhengzhou University between January 2022 and April 2023 were retrospectively analyzed. The dataset was randomly divided into a training set （174 cases） and a validation set （58 cases） at a ratio of 3∶1. Univariate and multivariate logistic regression analyses were performed to identify independent predictors influencing SLN metastasis. A nomogram was constructed， and its accuracy and clinical applicability were evaluated using receiver operating characteristic （ROC curve） analysis， calibration curve analysis， and decision curve analysis. Results The multivariate analysis revealed that palpability， CA153， calcification， and ALN blood flow signal were identified as independent risk factors for SLN metastasis （P < 0.05）. These four variables were integrated into a nomogram and plotted on the ROC curve. The area under the curves （AUCs） for the training set and validation set were 0.810 （95%CI： 0.744 ~ 0.876） and 0.737 （95%CI： 0.606 ~ 0.867）， respectively， indicating good predictive accuracy as demonstrated by the calibration curve. Conclusion Revised sentence： "Developing a nomogram for preoperative prediction of SLN metastasis in breast cancer patients offers a non-invasive approach for clinical application and serves as a reliable tool to identify breast cancer patients who may not require SLN biopsy， thereby facilitating decisions regarding further axillary lymph node dissection （ALND） and adjuvant therapy.