The importance of drug risk analysis is very evident in the ever-evolving landscape of pharmaceuticals and healthcare due to the introduction of innovative technologies. With the development of novel therapies and pharmaceutical innovations at an unprecedented rate, the challenges associated with ensuring the drug’s efficacy and safety have also increased. Promising advancement in drug risk analysis has been achieved with the integration of advanced technologies including machine learning and sophisticated data analytics. Making accurate predictions on the safety concerns of newly developed drugs by scrutinizing vast datasets has been made easy due to these advancements. Our research paper provides a versatile solution to analyse the risks of a particular drug that may have negative effects on individuals based on their individual medical histories and conditions. Using a handcrafted dataset containing health records of each patient’s medical history, we have trained a K-Nearest Neighbours (KNN) model using clustering methods that identify which patients may safely use this medication and who cannot with an accuracy of 84%. This sophisticated solution can help doctors determine the safety of a particular drug without causing any health complications with the existing medical conditions of the patient.