Objective: To investigate the clinicopathological factors influencing lung and bone metastasis in breast cancer, and to further construct a nomogram model for predicting the risk of lung and bone metastasis in breast cancer patients at various time points, followed by a prognostic analysis. Methods: The retrospective analysis included 200 patients with breast cancer, among whom 51 had lung metastases and 57 had bone metastases. The remaining 92 patients without metastases served as the control group. Baseline characteristics were analyzed using the chi-square test; COX univariate and multivariate analyses were applied to explore the influencing factors. A nomogram was constructed to predict the risk of individuals developing lung or bone metastasis at 1, 3, and 5 years. The predictive model was further validated by ROC curves and calibration curves, and decision curves were plotted to assess the clinical application value of the model. Results: Analysis revealed that age, BMI, tumor size, lymph node status, ER, PR, HER-2, and Ki67 significantly influenced lung metastasis (P < 0.05), while age, BMI, tumor size, lymph node status, ER, PR, and Ki67 significantly impacted bone metastasis (P < 0.05). The nomogram indicated that HER-2 negativity elevated the risk of breast cancer lung metastases. ROC curves were plotted for 1, 3, and 5 years, with AUC values and 95 % confidence intervals of 0.803 (67.42-93.15), 0.831 (75.93-90.29), and 0.854 (78.43-92.34) in the lung metastasis group, and 0.754 (55.15-95.66), 0.753 (64.91-85.71), and 0.777 (68.64-86.67) in the bone metastasis group, respectively. These results suggest that the model has a superior predictive efficacy and a high degree of predictive reliability. Additionally, the calibration curve demonstrated that the model is well-fitted, and the decision curve indicated that the model possesses clinical utility in practice. Conclusion: Age, BMI, tumor size, lymph node status, ER, PR, and Ki67 significantly influence lung and bone metastasis in breast cancer. The nomogram developed in this study can evaluate the risk of lung or bone metastasis for individuals at 1, 3, and 5 years, predict prognosis, guide clinical individualized treatment, and bring more benefits, further improving the quality of life for patients. It demonstrates good predictive ability and clinical value. Key message: The nomogram model constructed in this study can predict prognosis, guide clinical individualized treatment, and bring more benefits, further improving the quality of life for patients. It possesses good predictive ability and holds certain clinical predictive value.