Telemedicine has enhanced healthcare delivery by enabling treatment through advanced technologies. This research focuses on developing a virtual assistant to enhance telemedicine services, incorporating artificial intelligence techniques for accurate and efficient disease diagnosis and recommending disease specific specialist doctors. The present work employed a Decision Tree classifier to process the whole data about the patient symptoms for disease diagnosis. After the disease is diagnosed, a list of specialist doctors for the diagnosed disease is recommended. The proposed model gives 98% accuracy. The model has been compared with the existing RNN and CNN based systems and found to outperform those models on the given dataset. The proposed model demonstrates the scalability and adaptability for the diagnosis and symptoms across various medical domains, aiming to streamline telemedicine consultations and improve patient satisfaction.