Working collaboratively, we developed race-specific risk models using machine-learning techniques that incorporated clinical parameters, including biomarker data, in four community-based cohorts to predict the 10-year risk of incident heart failure in both Black and White adults. Our developed risk scores performed well in both internal and external validation cohorts and outperformed traditional heart failure risk equations. Additionally, our machine learning-based approach identified the important contributors to heart failure risk, which were unique to both Black and White adults. Taken together, race-specific and machine learning-based heart failure risk models developed by our group integrate clinical, laboratory, and biomarker data. They demonstrate superior performance when compared with traditional heart failure risk equations.