Background: Hypertension is one of the leading risk factors for cerebrovascular and cardiovascular diseases. Early identification of populations at risk of hypertension might be critical in managing hypertension, particularly in Sub-Saharan Africa, where the burden of hypertension is very high. This study aimed to develop a risk-scoring model for predicting hypertension among indigenous Africans. Methods: We used 4390 population-based (80% and 20% for training and validating the model, respectively) stroke-free controls from the SIREN study to develop and validate a risk-scoring model for hypertension among Africans. Fifteen risk factors were attempted to predict hypertension using multivariable logistic regression, and the beta coefficient of significant variables were weighted using a constant and standardized weighting procedure. A standardized score was generated between 0-and 100%, and the risk-score cut-off point was estimated using the receiver operating characteristics (ROC) curve, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and the Cohen’s kappa value at P<0.05. Results: Standardized and constant weight was applied to eight statistically significant risk factors, and the risk-score cut-off was 56% at a maximum Cohen’s kappa value of ≥0.71. The model performance had a ROC of 93.0% (95%CI: 92.0, 94.0), sensitivity of 84.8%, specificity of 87.9%, PPV of 90.6%, and NPV of 80.8% in the training data set. The validation data set had an ROC of 91.0% (95%CI: 89.0, 93.0), sensitivity of 80.0%, specificity of 92.8%, PPV of 91.1%, and NPV of 83.5%. Conclusion: The risk-scoring model for hypertension in this population was robust, sturdy with high predictive accuracy, and might be promising in the timely identification of populations at risk of hypertension for early prevention and management among indigenous Africans.