Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality worldwide. Public health responses to CVD require complex, multisectoral strategies that combine population-wide preventive interventions with individualised approaches. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this field, enabling more accurate diagnosis, prognosis and treatment personalisation. However, most AI applications remain confined to clinical domains, with limited translation into public health policy modelling.

This review aims to identify and synthesise recent evidence on the application of AI and ML systems for cardiovascular risk prediction and management, with a specific focus on their potential use in public health policy design and decision-making.

A systematic review will be conducted, registered in PROSPERO and reported following PRISMA guidelines. Searches will be performed in PubMed, Embase, Scopus, Web of Science, Bireme and Institute of Electrical and Electronics Engineers using standardised Descriptores en Ciencias de la Salud, Medical Subject Headings and Emtree terms. Eligible studies will include AI-based or ML-based models for cardiovascular risk prediction applied at a population, territorial or public health management level, published in English, Spanish or Portuguese within the last 5 years. Data extraction will consider article characteristics, health condition, AI/ML purpose, system features, Organisation for Economic Co-operation and Development classification, validation and performance and applicability to public health policy. Quality appraisal will use MINIMAR, DECIDE-AI or PROBAST-AI, depending on the study type. Data will be synthesised qualitatively, with descriptive frequencies and graphical summaries.

Ethical approval is not required as this study will be based on previously published data. Findings will be disseminated through peer-reviewed publications and policy-oriented forums involving the European Union and Latin American and Caribbean (LAC) academic stakeholders, with relevance for public health decision-making in Colombia and the LAC region.

CRD420251163276.