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With The Exponential Growth Of User-generated Health-related Content On Online Platforms, Patient Drug Reviews Have Become A Valuable Source Of Real-world Insights Into Drug Effectiveness And Side Effects. Traditional Drug Recommendation Systems Often Rely On Structured Clinical Data, Which May Not Fully Capture Patient Experiences And Sentiments. This Study Proposes A Drug Recommendation System Based On Sentiment Analysis Of Drug Reviews Using Machine Learning (ML) Techniques. The System Leverages Natural Language Processing (NLP) To Analyze Patient Reviews, Extracting Both Sentiment Polarity (positive, Negative, Neutral) And Key Opinion Aspects Related To Drug Efficacy, Safety, And Tolerability. Machine Learning Models Are Trained To Classify Sentiments And Predict Drug Suitability For Specific Conditions. By Integrating Sentiment-driven Insights With Recommendation Algorithms, The System Provides More Personalized And Patient-centric Drug Suggestions. Experimental Results On Benchmark Drug Review Datasets Demonstrate The Effectiveness Of The Proposed Approach In Improving Recommendation Accuracy Compared To Conventional Methods. This Work Highlights The Potential Of Combining Sentiment Analysis And ML To Support Informed Decision-making In Healthcare And Enhance Patient Outcomes.