Computer-based data prediction techniques play a very prominent role in medical fields. In the last few decades, various machine learning-based data prediction techniques have been used in medical files to detect various diseases and even offer automated suggestions through chatbots, which helps the user improve health outcomes. The primary target of the present work is the implementation of various machine learning algorithms for predicting diseases according to symptoms. Based on the user’s reported symptoms, the machine learning model predicts the disease; it can then provide suggestions from the specialist to the users and also book an appointment with the required doctor, if needed. The chatbot is also integrated into the proposed machine learning system, and based on this, it provides some immediate information on sickness based on user-given symptoms. The symptoms-based approach is more effective, can handle multiple symptoms, and provides more relevant information on the disease. It focuses on the gathering of algorithms and techniques for NLP and ML models such as Naive Bayes Classifier, Random Forest, Decision Tree, and Voting Classifier to analyze user-reported symptoms for disease detection and decision-making processes.