Abstract
In critical medical emergencies, timely and accurate drug recommendation is essential for
saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing
Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug
selections based on patient symptoms, medical history, and emergency condition. The system integrates
data from diverse medical databases, including symptoms, diseases, patient demographics, and prior
medical records, to recommend the most appropriate drugs or treatments in real-time. The ML model is
trained on historical medical data, utilizing algorithms such as Decision Trees, Random Forests, and
Neural Networks to predict optimal drug choices for different emergency situations. The system not only
considers the immediate medical needs but also factors in contraindications, potential drug interactions,
and patient-specific conditions, ensuring safer drug administration. This approach aims to reduce human
errors in emergency drug prescriptions, enhance the speed of medical response, and improve the overall
quality of care during emergencies. By leveraging data-driven insights, the system supports healthcare
professionals in making well-informed decisions, even in high-pressure situations. Ultimately, the Drug
Recommendation System serves as a valuable tool in healthcare, enhancing emergency medical care and
optimizing treatment outcomes.