This study investigates predicting the adverse side effects of a drug holds paramount importance due to the potential impact it can have on a patient's well-being. When a treatment extends its effects beyond the intended cure for a particular ailment, it can lead to the emergence of side effects. These side effects can vary in intensity, ranging from mild discomfort to severe, and in the worst-case scenarios, they can even prove fatal. Treatments can take various forms, including medications, surgical procedures, and alternative therapies, all of which can induce these unintended reactions. To ensure that patients can fully reap the benefits of long-term treatments, it is imperative to educate them about the possibility of encountering adverse events and to provide precautionary instructions before recommencing the treatment. It is noteworthy that many individuals on daily medication regimens experience adverse side effects, often due to factors such as the introduction of new drugs or adjustments in dosage. These side effects can manifest with varying degrees of severity and implications for the patient’s overall health. While physicians can anticipate some of these adverse effects based on their knowledge and past patient experiences, there still is a realm of unknown possibilities. The objective here is to empower medical professionals to better foresee and prepare for the negative side effects that may arise because of the drugs they prescribe. Achieving this aim can be facilitated through the application of artificial intelligence, which has the potential to enhance our ability to predict and manage these unintended reactions more effectively. So, In this study we are proposing a Machine Learning (ML) model such as Logistic Regression (LR), K-Nearest Neighbor (KNN) and Random Forest (RF) to predict the various side effects caused by various drugs on the human body.