The rates of drug addiction and overdose deaths in the United States have increased steadily from 1999-2022. However, many of the existing support and prevention resources rely heavily on the patient to navigate the complexities of seeking out support and treatment, adhering to schedules while managing various stressors (i.e. social, economic, health) and actively recovering. Such a burden contributes toward the high relapse rates. Significant effort has been made to increase accessibility and navigation of services, but more needs to be done to encourage and support patients, especially in moments of active crisis, or near crisis. An innovative approach spearheaded by the Healthcare Technology and Innovation Laboratory at the University of Toledo seeks to leverage existing smart technologies to facilitate real-time positive encouragement and support while monitoring patients between clinical visits and behavioral health interventions. This is achieved though the use of a wrist-worn wearable device (WWD) and software platform that leverages AI and machine learning algorithms predict relapse in opiod use disorder (OUD) patients. The WWD collects extensive multimodal data consisting of physiological, activity, lifestyle, and measures of affective state. Unlike commercial off-the-shelf (COTS) devices, the custom built WWD offers unrestricted data access at a significantly higher resolution, where critical trends can be extracted. The software processes and stores data locally on a mobile device and transfers this to the cloud. The software offers configurable and customizable processing which allows users to explore and identify patterns and trends in key data sources. The architecture is designed to support "plug-in" algorithms, including AI and machine learning to enable continuous refinement and adaptation with an emphasis on personalized medicine. Initial algorithms focus on assessing sleep quality, episodic stress/anxiety, detecting ambulatory states, and identifying deviations in lifestyle patterns. The initial use case for OUD patients includes algorithms providing a three-tiered alert system aimed at predicting relapse risk in OUD patients. In addition, the platform's broad applicability offers the potential for detection of symptoms of anxiety disorders and depression through existing raw physiological and accelerometer data processing. A pilot study is being conducted in Lucas County Ohio. Patients diagnosed with OUD patients and control subjects are being actively enrolled, and in addition, a comparison with an FDA approved COTS device is also being performed. The initial algorithms are logic-based and further machine learning and AI algorithms are being developed using data from the active pilot study. The availability of accessible real-time monitoring of OUD patients recovering to reduce relapse via a WWD could serve to increase effectiveness of the Department of Health and Human Services initiatives to combat drug addiction and relapse. The outcomes of the pilot study have the potential to complement larger existing initiatives with scalability at the national level. A key benefit of this approach is the use of technology for active monitoring and real-time intervention, a unique approach that helps to increase the efficacy of existing interventions and can be applied toward other mental health treatment domains.