This paper sets out to conduct a study on the application of machine learning algorithms in real-time DDI prediction in clinical scenarios to improve patient safety by minimizing adverse drug events. The study evaluates some of the most important models such as ANN, SVM, random forests, and decision trees for prediction through an exhaustive dataset on drug properties, patient data, and known interactions between drugs. This aims to develop predictive models of alerting clinicians in the prescription workflow more so with better accuracy and timeliness, thus bridging some of the important limitations of most DDI prediction systems, which usually base their output on static databases and cause alert fatigue. A systematic process of data accumulation, preprocessing of the same, model training, validation, and real-time implementation is used to keep the maximally robust models as efficient as possible. The results clearly show the former will have the highest accuracy, while the random forest model is the best in terms of both performance and computational efficiency. Such a machine learning model thus has shown huge potential in strengthening the decision-making of the clinical setup by ensuring that appropriate medical decisions are taken regarding medication management and further improved patient care.