Homeopathy is a system of medicine based on the principle: ‘like cures like’. Homeopathy provides as a cure for over 200 million people globally [1]. Traditionally a homeopathy remedy selection is primarily guided by materia medica [2] (a dataset of symptom-remedy pairs) which is subjective, slow, and heavily reliant on practitioner expertise. Due to variations in patient responses and the difficulty of integrating real-time feedback, current automated systems also face challenges in accurately matching complex symptom profiles to appropriate remedies, particularly. As part of this proposed framework to minimizing excessive testing and recommending the exact medication in fewer attempts, advanced data analytics, ensemble learning, and natural language processing (NLP) are used. On top of that, using AI and machine learning can really change the game when it comes to choosing the right remedy. This study proposes a framework that keeps learning and improving based on patient feedback and actual results from treatments. Turning the materia medica into a structured dataset and matching it with patient symptoms using Apache Spark to handle enormous patient data, and PyTorch helps with making smart predictions. Also by using natural language processing (NLP) to pull useful information from symptom descriptions, and passing through ensemble analytics, this framework mixes past data making the framework super dynamic and focused on real results.