This article summarizes recent developments in artificial intelligence-based detection, risk assessment, and prediction of cardiovascular diseases (CVDs) and highlights how novel advances support prevention through identification of high-risk individuals and personalized risk-reduction strategies. AI models demonstrated strong performance in CVD detection using echocardiographic and electrocardiographic data, while most effective predictive tools relied on clinical and demographic variables. Given the heterogeneity in the methodological quality of the studies, especially for data management practices, validation strategies, and performance reporting, the need for stricter adherence to reporting guidelines is highlighted. Finally, future research should focus on translating the models into clinical practice.