This project presents a personalized mental wellness support platform that combines a Seq2Seq-based NLP chatbot with a content-based recommendation system. Developed as a Flask web application, the chatbot interacts with users through natural conversations to understand emotional states and personal needs. User information collected from chats, quizzes, and journal entries is processed using techniques such as TF-IDF, topic modeling, and cosine similarity to generate personalized wellness resource recommendations. The integrated system demonstrates how conversational AI and intelligent recommendation algorithms can work together to provide accurate, relevant, and personalized mental health support.