Traditionally, providers and health systems have relied on claims-based risk-models, such as the CMS-HCC, ACG, and DxCG, which were built to forecast the risk of populations but not for individual patients. These models give a fairly good estimation of the risk of the population, but exhibit poor estimation if used to predict the risk at an individual level. Instead of relying solely on historical claims-based data for predicting the risk score of the patients, we can predict the future cost of care of a patient with much higher accuracy by integrating data from multiple sources such as EHRs, labs, pharmacy, and Social Determinants of Health by applying advanced machine learning techniques. This research paper explores the basics of risk scoring and stratification, historical models of risk determination, and how cutting-edge ML techniques such as AI and advanced regression techniques are instrumental parts in the transformation to value-based care, from eliminating variations in care quality to ensure accurate reimbursements. Also, it highlights Innovaccer’s approach in estimating the future cost of care based on past medical history, clinical and socio-economic data, and many other factors.