Objectives: There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of healthcare. Methods: This research focuses on developing a mobile application for obesity risk assessment using wearable technology and proposing an individualized activity/dietary plan. From calculating the Body Mass Index, we established an individualized health profile and used the average data collected by a smart vest to offer the level of activity and health goals. Results: We developed an algorithm to assess the risk of obesity using the individual’s current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect data from the wearable vest and user-reported data. Based on the collected data, the proposed application assessed the risk of obesity/ overweight, measured the current activity level and recommended an optimized calorie plan. Conclusion: The proposed model can integrate data from multiple sources including sensors, wearable garment, medical devices and also the manually entered (user reported) data. The model (and its rule-based engine) will continuously self-learn and tune the model for better accuracy and reliability over-time.