This presentation proposes a next-generation regulatory framework for opioid analgesics that integrates real-world data, adaptive licensing and labelling, and community-driven surveillance to overcome the shortcomings of traditional, static regulatory approaches. The framework is built on four pillars: first, an AI-augmented surveillance system that combines clinical data with social determinants of health to dynamically identify high-risk areas; second, adaptive licensing with evolving labels that use continuous real-world data submissions to update risk-benefit profiles in near-real time; third, pharmacist-led surveillance networks employing secure, automated reporting systems to enhance early detection of misuse; and fourth, the incorporation of harm reduction metrics through partnerships with community organizations and non-traditional data sources. This dynamic, process-oriented approach enables timely regulatory adjustments, ensures better alignment with FDA's REMS and post-marketing requirements (PMRs), and addresses ethical concerns related to AI bias and patient privacy. By proposing a framework under the FDA's Opioid Data Initiative, this study aims to provide actionable recommendations for policymakers and stakeholders to mitigate opioid misuse and improve public health outcomes.