Cardiovascular diseases are a leading cause of morbidity and mortality in France, despite current preventive strategies and evidence-based therapies. Artificial intelligence (AI) has emerged as a promising approach to overcoming several of these barriers, with the potential to improve cardiovascular prevention across the entire spectrum of care. Upstream, AI can enhance risk stratification through the integration of clinical, biological, imaging, environmental, behavioural and longitudinal data, thereby moving cardiovascular prevention toward an AI-augmented precision medicine paradigm, in which risk assessments are increasingly individualized. Downstream, AI-enabled clinical decision support systems may improve guideline implementation, reduce therapeutic inertia and support individualized treatment optimization. Generative AI may also strengthen patient education, personalize risk communication, promote healthy behaviours and improve long-term adherence through adaptive coaching and continuous monitoring. However, translating these promises into meaningful improvements in cardiovascular outcomes will require overcoming substantial technological, organizational, regulatory and societal challenges. These challenges include ensuring data quality and information system interoperability, addressing inequalities in access to digital health technologies, improving the acceptability of AI among patients and healthcare professionals through rigorous evaluation and, critically, adapting healthcare systems to absorb, triage and manage the potentially large number of at-risk individuals identified by AI-enabled prevention strategies. By examining these barriers, this review highlights that AI mainly shifts the bottlenecks of prevention downstream, rather than removing them, and outlines the conditions required for AI, beyond intrinsic performance, to improve cardiovascular prevention in routine clinical practice.