Background: Luminal A breast cancer (LABC) is the most common subtype with bone metastasis. Identifying high-risk patients for bone metastasis early is essential for improving outcomes. This research focused on creating and validating a nomogram to assess the risk of BM in patients with LABC. Methods: We extracted data for 236,132 LABC patients from the SEER database covering the years 2010 to 2015. Patients diagnosed between 2010 and 2013 composed the training set (n=152,850), whereas those diagnosed between 2014 and 2015 composed the validation set (n=83,282). Logistic regression analyses identified predictive factors for BM. A nomogram was developed and validated through ROC curve analysis and calibration plots. A total of 2.1% of the training cohort and 2.2% of the validation cohort developed BM. T stage, N stage, and marital status were significant predictors of BM risk. The nomogram exhibited strong discriminative performance, with an AUC of 0.894 (95% CI: 0.890-0.899) in the training set and 0.846 (95% CI: 0.837-0.856) in the validation set. The calibration plots demonstrated strong concordance between the predicted and observed BM rates across both cohorts. Conclusion: This study established a clinically relevant nomogram for predicting BM risk in LABC patients. The model’s strong predictive ability suggest its potential as a valuable tool for risk stratification and personalized patient management. Further external validation is warranted to confirm its generalizability across diverse populations. This study focused on developing a risk prediction model for bone metastasis in LABC patients using a nomogram based on data from the SEER database