The Medicare Chatbot improves healthcare efficiency and accessibility for users by providing quick, personalized Medicare information. This paper describes a Chatbot aimed to aid in disease diagnosis by analysing user-input symptoms and making predictions about likely diseases. The Chatbot also provides advice on healthy eating and makes it easier to schedule doctor appointments. To accomplish this, The Convolutional Neural Network (CNN) algorithm has been trained using a vast dataset that maps disease names (in the ‘Source’ column) to associated symptoms (in the ‘Target’ column). By assessing these symptoms, the Chatbot can reliably forecast diseases and provide personalized food suggestions and home cures. According to performance measures, the Chatbot using the CNN algorithm outperforms the Chatbot using the SVM algorithm. The integration of the CNN algorithm enhances the Chatbot’s diagnostic accuracy by efficiently recognizing complex patterns in symptom data. This unique technique provides a potential step toward accessibility.