Cardiovascular Diseases (CVDs) remain the leading cause of mortality worldwide despite significant advances in diagnosis and treatment, underscoring the urgent need for more effective prevention strategies. Conventional risk assessment models and population-based interventions often fail to capture the complex interplay of genetic, behavioural, environmental and social determinants that influence cardiovascular risk. Recent advances in Artificial Intelligence (AI), including machine learning, deep learning, generative AI, foundation models and multimodal AI, have created unprecedented opportunities to transform cardiovascular disease prevention through more accurate risk prediction, early disease detection, personalized lifestyle interventions, continuous remote monitoring and data-driven clinical decision-making. By integrating diverse data sources such as electronic health records, medical imaging, wearable devices, genomics and social determinants of health, AI supports the emerging paradigm of Precision Public Health, enabling targeted interventions at both individual and population levels. This review synthesizes recent evidence on AI applications across the cardiovascular prevention continuum, including risk prediction, digital health, wearable technologies, cardiovascular imaging, big data analytics and population surveillance. It also discusses the ethical, regulatory and implementation challenges associated with AI adoption, particularly in low- and middle-income countries, while highlighting recent policy initiatives and digital health advancements. Finally, the review identifies current research gaps and future directions, emphasizing the importance of explainable, equitable and clinically validated AI solutions. The responsible integration of AI within a Precision Public Health framework has the potential to reshape cardiovascular prevention, improve health equity and substantially reduce the global burden of cardiovascular disease.
