• Mr.Harsha Vardhan Gangumolu
• Gosala Sanjana
• Tupakula Veera Venkata Satya Vineeth
• Vegesana Jayadeep Varma
• Sheik Reshma

Timely and precise drug suggestions are important in enhancing patient outcomes, especially in emergency medical situations. This paper introduces a machine learning-powered drug recommendation system designed to assist healthcare professionals in making well-informed treatment decisions. This system employs Random Forest and Decision Tree Classifiers, recognized for their high accuracy and interpretability. Unlike conventional recommendation systems that analyze limited symptoms and suggest a single drug, our approach expands the scope by considering 30 distinct medical parameters. The dataset, comprising 1,200 records spanning 10 disease categories such as Allergy, Diabetes, Jaundice, and Pneumonia, ensures comprehensive coverage of common health conditions. The trained model achieved 100% accuracy on both training and testing datasets, demonstrating its reliability. By integrating machine learning techniques, the system not only recommends the most suitable medications based on patient symptoms but also provides disease predictions, enhancing diagnostic accuracy.

Drug Recommendation, Machine Learning, Healthcare Decision System, Decision Tree Classifier, Random Forest Classifier.

"Drug Recommendation System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.g641-g647, March-2025, Available :http://www.jetir.org/papers/JETIR2503677.pdf

"Drug Recommendation System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppg641-g647, March-2025, Available at : http://www.jetir.org/papers/JETIR2503677.pdf