In telemedicine, giving timely and accurate initial medical advice can improve patient outcomes and reduce the strain on healthcare systems. “E Consult  “ is a chatbot designed to help patients by answering common medical questions about wounds and symptoms like fever, cuts, and blisters. A neural network model and Natural Language Processing (NLP) techniques are used by the chatbot to transform user inputs into a structured manner. On the basis of established medical intentions, it then offers pertinent answers. The chatbot was trained using a proprietary neural network architecture on a dataset with a variety of intents and replies. In order to enable precise intent classification, the system preprocesses incoming data utilizing bag-of-words, tokenization, and stemming techniques. The Adam optimizer was used to optimize the neural network, which consists of input, hidden, and output layers, after it was trained using the CrossEntropyLoss function. Our results show that the chatbot has a confidence level that guarantees dependable responses and can correctly classify user intents. This work offers a scalable method for first patient triage and basic medical guidance, which has significant telemedicine implications. It reduces needless trips to medical experts and improves access to healthcare information, making it a fundamental component of increasingly extensive telehealth platforms.