There is compelling evidence of significant country-level disparities where African countries, particularly South Africa, have the highest hypertension rates in the world. To develop and validate a simple risk scoring algorithm for hypertension in a large cohort (80,270) of South African men and women. Multivariable logistic regression models were used to build our hypertension risk scoring algorithm and validated externally and internally using the standard statistical techniques. We also compared our risk scores with the results from the Framingham risk prediction model for hypertension. Six factors were identified as the significant correlates of hypertension: age, education, obesity, smoking, alcohol intake and exercise. A score of = 25 (out of 57) for men and = 35 (out of 75) for women were selected as the optimum cut-points with 82% (43%) and 83% (49%) sensitivity (specificity) for males and females, respectively in the development datasets. We estimated probabilities of developing hypertension using the Framingham risk prediction model, which were higher among those with higher scores for hypertension. Identifying, targeting and prioritising individuals at highest risk of hypertension will have significant impact on preventing severe cardiometabolic diseases by scaling up healthy diet and life-style factors. Our six-item risk scoring algorithm may be included as part of hypertension prevention and treatment programs by targeting older individuals with high body fat measurements who are at highest risk of developing hypertension.