Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. Early identification of individuals at high risk is a fundamental strategy for reducing cardiovascular morbidity and mortality. Cardiovascular risk prediction models have become essential tools in primary care settings, enabling healthcare providers to estimate an individual's probability of developing cardiovascular events and to guide preventive interventions. This study reviews the development, application, performance, and clinical utility of major cardiovascular risk prediction models used in primary healthcare. Through a comprehensive review of published literature, clinical guidelines, and risk assessment frameworks, the study evaluates widely used models such as the Framingham Risk Score, SCORE2, QRISK3, Reynolds Risk Score, and ASCVD Risk Estimator. Findings indicate that risk prediction models improve preventive decision-making, facilitate personalized care, and support population health management. However, limitations related to population diversity, calibration, data quality, and implementation barriers remain significant challenges. The study concludes that integrating advanced analytics, electronic health records, and artificial intelligence may enhance future cardiovascular risk prediction and improve patient outcomes.

Keywords: Cardiovascular Disease, Risk Prediction, Primary Care, Framingham Risk Score, QRISK3, ASCVD, Preventive Medicine