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Rice Is One Of The Most Important Staple Crops Worldwide, And Its Productivity Is Often Threatened By Various Leaf Diseases Such As Bacterial Blight, Blast, And Brown Spot. Early And Accurate Identification Of These Diseases Is Crucial For Effective Crop Management And Yield Improvement. Traditional Disease Detection Methods, Which Rely On Manual Field Inspection, Are Time-consuming, Labor-intensive, And Prone To Human Error. To Overcome These Limitations, This Study Proposes An Automated Rice Leaf Disease Prediction System Based On MobileNetV2, A Lightweight Deep Learning Model Optimized For Mobile And Embedded Devices. The Model Leverages Transfer Learning To Extract Discriminative Features From Rice Leaf Images And Classify Them Into Healthy Or Diseased Categories. MobileNetV2’s Efficient Architecture Significantly Reduces Computational Cost While Maintaining High Classification Accuracy, Making It Suitable For Real-time Deployment In Agricultural Environments. Experimental Results Demonstrate That The Proposed System Achieves Robust Performance With High Accuracy, Precision, And Recall, Thereby Offering A Reliable And Scalable Solution For Smart Farming. This Work Highlights The Potential Of Integrating Deep Learning With Mobile Technologies To Support Farmers In Disease Monitoring And Decision-making, Ultimately Contributing To Sustainable Agriculture.