In today’s healthcare sector, online recommender systems have become increasingly prevalent among doctors, medical professionals, and patients alike. With the majority of consumers turning to online resources for prescription suggestions, these systems play a crucial role in aiding decision- processes, particularly during times of crises such as pandemics or natural disasters. Leveraging the power of machine learning (ML), these systems offer enhanced accuracy, precision, and reliability in clinical predictions while optimizing resource utilization. By considering various patient parameters like symptoms, blood pressure, and diabetes, medication recommendation systems provide personalized and trustworthy guidance on medication selection, dosage, and potential adverse effects. Among the different ML approaches, Decision Tree-based systems demonstrate superior accuracy, making them particularly valuable in emergency situations where swift and safe medication recommendations are essential.