Abstract

------------------------------------------------------------------------------***-------------------------------------------------------------------------------------- Abstract: This paper presents Vital – a Smart Wellness and Nutrition Recommendation System, a web-based platform designed to provide personalized supplement and nutrition suggestions. The system uses machine learning (ML) and natural language processing (NLP) techniques to analyze user profiles and wellness-related inputs. Users enter details such as gender, dietary preferences, lifestyle, allergies, and health concerns to receive tailored recommendations that align with their needs. The platform processes free-text symptom descriptions using vector-based similarity models and applies dietary and allergybased constraints to ensure safe recommendations. Vital also incorporates a feedback mechanism where user responses are stored and used to improve future suggestions. The system demonstrates a full-stack implementation using React.js, Node.js, Python-based ML services, and MongoDB, integrating structured datasets with real-time user interaction. Vital aims to support informed wellness decisions while emphasizing that professional medical consultation remains essential before using any supplements or nutrition plans