Background: Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aimed to develop a model for absolute breast cancer risk prediction for Nigerian women. Methods: A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998–2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. Results: The NBCS model included age, age at menarche, parity, duration of breastfeeding, family history of breast cancer, height, body mass index, benign breast diseases, and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model [area under ROC curve (AUC) = 0.703, 95% confidence interval (CI), 0.687–0.719] was better than the Black Women's Health Study (BWHS) model (AUC = 0.605; 95% CI, 0.586–0.624), Gail model for white population (AUC = 0.551; 95% CI, 0.531–0.571), and Gail model for black population (AUC = 0.545; 95% CI, 0.525–0.565). Compared with the BWHS and two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45%, and 14.19%, respectively. Conclusions: We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in Sub-Saharan Africa populations. Impact: Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high risk for breast cancer screening. Cancer Epidemiol Biomarkers Prev; 27(6); 636–43. ©2018 AACR .