<![CDATA[The increasing availability of large-scale health data has created opportunities for using data mining to support evidence-based decision-making and public health policy development. This narrative review aims to describe the role of data mining in identifying public health priorities, supporting healthcare decision-making, facilitating program planning and evaluation, and strengthening evidence-based health policies. Relevant literature published between 2024 and 2026 was identified through searches of the Scopus and PubMed databases using keywords related to data mining, machine learning, predictive analytics, health policy, decision-making, and public health. Eligible articles were selected based on their relevance and synthesized narratively. The findings indicate that data mining techniques, including machine learning, predictive analytics, classification, clustering, and knowledge discovery, are widely used to identify disease patterns, high-risk populations, healthcare needs, and to support resource allocation and program evaluation. These approaches improve the targeting of public health interventions and strengthen evidence-based policy development. However, challenges related to data quality, interoperability, privacy protection, and model interpretability remain. Strengthening data governance and analytical capacity is essential to optimize the use of data mining in public health.]]>
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