The increasing demand for efficient healthcare services and the limitations posed by a shortage of medical professionals have accelerated the development of intelligent healthcare solutions. This research presents a healthcare chatbot designed using Support Vector Machines (SVM) and decision tree algorithms to deliver accurate and timely disease predictions based on user-provided symptoms. The chatbot is aimed at bridging the gap between healthcare providers and patients, offering an accessible platform that mimics the interactions of a human medical professional. By leveraging machine learning techniques, the chatbot can classify symptoms, provide medical guidance, and recommend further medical consultation when necessary. The SVM model categorizes symptoms to identify potential health issues, while the decision tree algorithm predicts diseases and suggests treatment paths based on input data. The system was tested using real-world healthcare datasets to ensure accurate disease prediction and effective user interaction. Experimental results demonstrate that the chatbot achieves high accuracy in predicting diseases, significantly outperforming traditional rule-based systems. The system offers improved response times and enhanced user engagement by providing personalized, context-aware recommendations. Despite its advantages, limitations such as model retraining requirements and data biases were observed, paving the way for future enhancements.In conclusion, this chatbot represents a scalable and user-friendly solution to healthcare challenges, especially in regions with limited medical resources. By providing timely assistance, it has the potential to alleviate pressure on healthcare systems, improve patient outcomes, and foster better access to medical guidance. Future work will further integrate advanced NLP capabilities, expand its disease database, and refine user interaction mechanisms to enhance its utility and accuracy.