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Objectives: There is currently a limited ability to accurately identify women at risk of postpartum hemorrhage (PPH). We conducted the "Predict‐PPH" study to develop and evaluate an antepartum prediction model and its derived risk‐scoring system. Methods: This was a prospective cohort study of healthy pregnant women who registered and gave birth in five hospitals in Lagos, Nigeria, from January to June 2023. Maternal antepartum characteristics were compared between women with and without PPH. A predictive multivariable model was estimated using binary logistic regression with a backward stepwise approach eliminating variables when P was greater than 0.10. Statistically significant associations in the final model were reported when P was less than 0.05. Results: The prevalence of PPH in the enrolled cohort was 37.1%. Independent predictors of PPH such as maternal obesity (adjusted odds ratio [aOR] 3.25, 95% confidence interval [CI] 2.47–4.26), maternal anemia (aOR 1.32, 95% CI 1.02–1.72), previous history of cesarean delivery (aOR 4.24, 95% CI 3.13–5.73), and previous PPH (aOR 2.65, 95% CI 1.07–6.56) were incorporated to develop a risk‐scoring system. The area under the receiver operating characteristic curve (AUROC) for the prediction model and risk scoring system was 0.72 (95% CI 0.69–0.75). Conclusion: We recorded a relatively high prevalence of PPH. Our model performance was satisfactory in identifying women at risk of PPH. Therefore, the derived risk‐scoring system could be a useful tool to screen and identify pregnant women at risk of PPH during their routine antenatal assessment for birth preparedness and complication readiness. Synopsis: We recorded a postpartum hemorrhage (PPH) prevalence of 37.1%. The developed prediction model in the study performed satisfactorily in identifying women at risk of PPH. [ABSTRACT FROM AUTHOR]