The paper presents a BERT-based medical chatbot designed to enhance healthcare communication and accessibility. The chatbot leverages advanced deep learning technology, particularly BERT, to address traditional challenges such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to provide personalized feedback. The chatbot's performance is evaluated using various metrics, including accuracy, precision, AUC-ROC, recall, and F1-score, achieving high scores of 98%, 97%, 97%, 96%, and 98%, respectively. These metrics highlight the chatbot's effectiveness in handling medical queries, predicting specific diseases, and ensuring comprehensive coverage of potential conditions. The study also discusses the advantages and disadvantages of the proposed model, emphasizing its potential to revolutionize healthcare communication and information accessibility. Future improvements include multilingual support and enhanced data privacy and security measures.The paper presents a BERT-based medical chatbot designed to enhance healthcare communication and accessibility. The chatbot leverages advanced deep learning technology, particularly BERT, to address traditional challenges such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to provide personalized feedback. The chatbot's performance is evaluated using various metrics, including accuracy, precision, AUC-ROC, recall, and F1-score, achieving high scores of 98%, 97%, 97%, 96%, and 98%, respectively. These metrics highlight the chatbot's effectiveness in handling medical queries, predicting specific diseases, and ensuring comprehensive coverage of potential conditions. The study also discusses the advantages and disadvantages of the proposed model, emphasizing its potential to revolutionize healthcare communication and information accessibility. Future improvements include multilingual support and enhanced data privacy and security measures.