Objective: A nomogram model is developed based on ultrasound features to provide imaging evidence for enhanced clinical assessment of axillary lymph node metastasis in patients with lateral quadrant breast cancer. Methods: We retrospectively analyzed ultrasonographic features of axillary lymph nodes and primary tumors, which were confirmed by pathology, in patients diagnosed with breast adenocarcinoma. Univariate and multivariate logistic regression analyses were performed to identify risk factors for lymph node metastasis, and a nomogram model was constructed to assess the presence of axillary lymph node metastasis. P value less than 0.05 indicated statistically significant differences. Results: The study cohort comprised 127 patients with breast cancer, of which 54 cases were classified as positive group for axillary lymph node metastasis and 73 cases were classified as negative group for without axillary lymph node metastasis. Multivariable logistic regression analysis revealed that tumor spiculated margins (OR=4.16, 95% CI: 1.25-13.79, p=0.020) and unclear lymphatic gate structure (OR=19.20, 95% CI: 1.98-186.36, p=0.011) independently contributed to the risk of axillary lymph node metastasis in lateral quadrant breast cancer patients. Furthermore, a nomogram model was developed to predict axillary lymph node metastasis in lateral quadrant breast cancer cases. The receiver operating characteristic curve (ROC) demonstrated an area under the curve (AUC) of 0.74 (0.62-0.86 ) for the training set and an AUC of 0.73(0.62-0.84 )for the validation set. Hosmer-Lemeshow test results indicated no significant deviation from goodness-of-fit for both the training set and validation set with p-values of p = 0 .570 and p = 0.552 respectively. Conclusion: Ultrasound plays a valuable role in the assessment of axillary lymph node metastasis in preoperative lateral quadrant breast cancer. Our logistic regression-based nomogram model is both secure and dependable, making it an exceedingly practical tool.