Introduction: Community-level social determinants of health (SDOH) can facilitate the identification of vulnerable populations with poor health outcomes. Hypothesis: We assess whether machine learning methods utilizing area-level SDOH indicators can identify populations at higher risk for CV mortality. Methods: 144 SDOHs were extracted from the SDOH database compiled, at a county level, by the Agency for Healthcare Research and Quality between 2009-2018. The Person’s correlation coefficient between SDOH with CV mortality was used to select 12 key SDOH (p < 0.05) and linear and nonlinear models were tested to build a predictive model for CVD mortality rates per 100,000 people for each county from SDOH. Datasets were randomly split in 80/20 for training and validation, and the accuracy of the models was compared in terms of mean relative error (MRE). Results: SDOH related to income (median household income), poverty (fraction population with a poverty ratio more than 2.00), and educational attainment (high school, BSc, MSc, doctorate) were the principal factors affecting CV mortality. Factors related to race/ethnicity, living conditions (children living with grandparents), and physical infrastructure (median household value and fraction of mobile homes) were also included in the model. The most performant regression method is the Bayesian optimized artificial neural network (Figure). The performance of linear models (linear elastic-networks, MRE = 17.6%; linear regression, MRE = 38.8%) was inferior to nonlinear models. Among nonlinear models, optimized Bayesian feedforward neural networks OBFNN presented the best performance (MRE = 9.7%). Conclusions: We developed a county-level risk score for cardiovascular mortality using 12 SDOH including demographics, income and poverty, education, and physical infrastructure. Future work should look at extending beyond CVD mortality and combining this SDOH-based score for patient-level cardiovascular risk.