This study examines the integration of artificial intelligence (AI) into liberal drug policy frameworks, focusing on its emerging applications, normative implications, and governance challenges. Drawing on an intersection-focused qualitative evidence synthesis combined with comparative case study analysis, the study synthesizes emerging applications in drug-use monitoring, regulated supply-chain oversight, and harm reduction. While AI offers significant potential to enhance policy responsiveness and regulatory efficiency, its deployment also raises critical concerns related to algorithmic bias, data protection, and institutional accountability. To address these tensions, the study proposes a multidimensional evaluative framework based on five criteria: effectiveness, fairness, transparency, privacy protection, and ethical alignment. The findings underscore the need for context-sensitive regulatory mechanisms that safeguard human rights, enable participatory oversight, and mitigate algorithmic harms. By integrating normative theory with structured evidence screening and comparative analysis, the study contributes a conceptually grounded framework for evaluating AI-driven governance in high-stakes public health and regulatory contexts.
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