Cardiovascular diseases remain a leading cause of mortality and disability worldwide, creating an urgent need for effective risk prediction and preventive healthcare strategies. Artificial intelligence (AI) has emerged as a promising approach for analyzing complex medical data, identifying cardiovascular risk factors, and supporting early intervention. This article examines the application of AI and machine learning in intelligent cardiovascular risk prediction and prevention, emphasizing predictive algorithms, clinical applications, and implementation challenges. Techniques such as random forests, support vector machines, gradient boosting, and artificial neural networks can identify patterns within demographic information, clinical measurements, laboratory results, and lifestyle data. These capabilities may support the identification of high-risk individuals and improve the personalization of preventive interventions. Existing research also demonstrates the potential of combining machine learning with explainable artificial intelligence techniques, including Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to improve the transparency of cardiovascular predictions (Lundberg & Lee, 2017; Ribeiro et al., 2016).[5, 9] The article adopts a structured literature review methodology to examine published research on cardiovascular prediction models, risk factors, prevention strategies, and clinical decision support. The reviewed evidence highlights opportunities for improving risk stratification, identifying previously unrecognized patterns, and supporting timely preventive care. However, challenges involving data quality, algorithmic bias, model interpretability, privacy, and generalizability remain significant. Furthermore, predictive performance alone does not establish clinical usefulness, and AI-generated estimates require appropriate validation and professional oversight. The article concludes that responsible integration of artificial intelligence into cardiovascular healthcare may strengthen early risk identification and personalized prevention when supported by representative datasets, rigorous evaluation, and evidence-based clinical practice. AI should complement established medical assessment rather than replace healthcare professionals' judgment.