Cardiovascular disease remains a leading cause of death among individuals with Type 2 diabetes mellitus (T2DM). To better direct preventative care, many risk assessment tools have been developed to determine whether T2DM patients will develop cardiovascular disease. This paper analyzes three specific tools: UKPDS (United Kingdom Prospective Diabetes Study), the Framingham Risk Score, and the QRISK (QRESEARCH cardiovascular risk algorithm) calculator, with a focus on their predictive utility and limitations in clinical practice. All these models were designed using a variety of patient information, including demographics, cholesterol levels, and blood pressure values; however, each has its own drawbacks. The UKPDS is based on an outdated study and does not account for patients with a history of baseline heart disease. The Framingham Risk Score is less accurate when determining the risk of cardiovascular events in underserved populations, as the database it was developed from lacks sufficient representation of these groups. While the QRISK calculator incorporates additional patient factors to better address the gaps of previous tools, resulting in more accurate predictions, none of these tools are perfectly individualized. Patient data is constantly changing, highlighting the need for models that utilize machine learning algorithms. Such approaches allow for greater adaptability and can integrate data from biomarkers and continuous glucose monitoring devices. Improving the accessibility and implementation of these newer tools is essential to better address existing healthcare gaps and enhance preventative care in high-risk populations.