This capstone project applied machine learning techniques to Medicare opioid data to identify high-risk regions, predict trends in opioid prescriptions, and analyze the impact of opioid treatment providers. The primary objective was to provide healthcare providers and policymakers with a tool to improve resource allocation, reduce opioid misuse, and improve public health. This study examined various factors contributing to opioid misuse by focusing on geographic regions, treatment provider access, and prescription trends over time. The project aimed to develop actionable recommendations for stakeholders involved in managing opioid-related challenges by applying predictive analytics and clustering techniques.