Our team proposed HuskyFit as an app for fellow UConn students that will motivate students to adopt healthier lifestyles to improve their mental and physical health. HuskyFit cultivates a personalized experience for UConn students where they can log dining hall foods, receive food suggestions, and track exercise. HuskyFit promotes healthier lifestyles within the UConn community, benefiting the mental and physical well being of students on campus. HuskyFit’s front end is written in JavaScript using React Native. The backend consists of a REST API (API Gateway), no SQL databases (DynamoDB), caching (S3), login and authentication (Cognito), a food recommendation machine algorithm (SageMaker), and event driven compute services (Lambda and EventBridge). With web scraping Python scripts, HuskyFit collects data from UConn Dining Hall menus. Nutritional information for all dining hall meals is available. The user can add food to a food log to track calories and macronutrients. The app provides well organized visuals with a graph that shows progress on macronutrient goals and a food log with calories and macronutrients. HuskyFit also provides fitness based features. The user enters intensity and time elapsed for that exercise. Entering calories is optional for the user; The user can leave that field empty and HuskyFit will compute an estimated amount of calories burnt for the user. The workout log screen lists the user's workouts for the current week. The home screen visualizes the food recommendation machine learning algorithm. It takes into account the user's dietary restrictions, preferred dining halls, and other factors in order to provide personalized food recommendations. Having a user friendly interface with clear and simple visualizations for the user is motivating for the user to further develop healthy lifestyle choices. HuskyFit serves the UConn student with an easy to interact app with information and resources to become a healthier, happier UConn student.