Background: The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness. Methods: From 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and were randomly assigned to the training cohort and the validation cohort. Another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan–Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts. Results: LASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The R showed that the OS of low-risk patients was considerably better than that of high-risk patients (P<0.001). Conclusions: The nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.