A chatbot, or conversational agent, is an AI-powered software designed to communicate with users using natural language. In the healthcare domain, medical chatbots play a crucial role in providing preliminary health guidance, answering medical queries, and assisting patients with symptom analysis. One of the major challenges in developing a medical chatbot is designing an effective dialogue system that can accurately understand user input and provide relevant responses. Early chatbot models relied on rule-based systems and statistical methods, which had limited flexibility in handling diverse conversations. However, with advancements in artificial intelligence and natural language processing (NLP), deep learning techniques, particularly end-to-end neural networks, have revolutionized chatbot development. Since 2015, encoder-decoder recurrent models, originally designed for neural machine translation, have become dominant in conversational AI due to their ability to generate meaningful and context-aware responses. This project aims to develop a medical chatbot using NLP techniques to enhance healthcare accessibility. The chatbot will leverage deep learning models to analyze user queries, understand symptoms, and provide preliminary advice based on medical knowledge. By integrating state-of-the-art advancements in NLP, this system seeks to offer accurate, reliable, and interactive healthcare assistance, improving user experience and bridging the gap between patients and medical professionals.