Patient pooling has been a major problem in the field of drug discovery and drug investigation. Even what is more daunting, is to provide a large scale solution for the classification of diseases and find side effects of personalised or precision medicine by clustering the pool and find similar investigations for pharmacovigilance, drug discovery and precision medicine. This can be solved by generating patterns through machine learning and deep learning models to find the common pools of similar pattern and diagnosis from clusters and distribute it by mobile application for the large scale patients clustering.This method is presented for Precision medicine, Pharmacovigilance and Drug discovery. Patients raw data is processed for classification and for personalised medicine. Patients collective information stored in database warehouses for clustering and applying advanced machine learning models on it will help in pharmacovigilance and early information regarding demographic disease epidemics. Patients diagnosis clustering can help to find out the pattern for drug discovery with respect to the geographical location and similar characteristics which have been found effective and will reduce time in drug discovery.