Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, optimizing treatment plans, and enabling data-driven public health strategies. This paper focuses on the application of AI to combat the opioid overdose crisis in the United States, utilizing machine learning techniques—particularly reinforcement learning—to inform policy interventions. We explore the use of deep Q-networks (DQN) trained on CDC opioid mortality data to simulate optimal intervention strategies across states, demonstrating a potential reduction of 15% in projected deaths. Additionally, we apply clustering and trend analysis on multi-year overdose data to uncover regional dynamics and emerging hotspots. The study also examines key ethical considerations, data privacy solutions, and integration challenges within AI-driven healthcare systems. By combining algorithmic insights with epidemiological realities, this work offers a scalable and explainable AI framework to support equitable healthcare policy design.