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Coffee Is One Of The Most Important Commercial Crops Worldwide, Contributing Significantly To The Global Economy And Agricultural Livelihoods. However, Its Productivity Is Severely Threatened By Various Leaf Diseases Such As Coffee Leaf Rust, Cercospora Leaf Spot, And Coffee Berry Disease, Which Lead To Substantial Yield Losses And Reduced Bean Quality. Early And Accurate Detection Of These Diseases Is Crucial For Effective Crop Management And Sustainable Coffee Production. Traditional Disease Diagnosis Methods Rely On Manual Inspection, Which Is Labor-intensive, Time-consuming, And Often Prone To Human Error. To Address These Challenges, This Study Proposes An Automated Coffee Leaf Disease Detection System Using Deep Learning Algorithms. Convolutional Neural Networks (CNNs) And Transfer Learning Models Such As VGG16, ResNet50, And MobileNetV2 Are Employed To Extract Discriminative Features From Leaf Images And Classify Them Into Healthy And Diseased Categories. The Proposed Approach Enhances Accuracy, Reduces Dependency On Expert Knowledge, And Enables Real-time Disease Monitoring. Experimental Results Demonstrate That Deep Learning–based Models Outperform Conventional Methods, Achieving High Precision, Recall, And Overall Classification Accuracy. This Study Highlights The Potential Of AI-driven Solutions In Supporting Precision Agriculture And Ensuring The Sustainable Cultivation Of Coffee.