Encouraging a healthy lifestyle and avoiding chronic diseases is a persistent public health goal. Particularly, when societies with considerably higher living standards are also affected by sedentary norms. Taking forward the existing Health App project, this thesis enriches the functionality of the app with a Large Language Model-powered personalized recommendation generator. In this thesis, the challenge of developing an engine that provides applicable, comprehensive, and individualized health and wellness advice is addressed. A cloud ready and modular recommender system is designed and deployed which can assimilate seamlessly with the already existing Health App’s mobile platform. By exploiting heterogeneous user data which includes metrics and logs related to physical activity, sleep, nutrition, and stress which is either acquired by Fitbit, and manually entered by the user, the recommender system yields customized guidance to enhance the health and well-being of the user.