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Chili (Capsicum Spp.) Is One Of The Most Widely Cultivated Spice Crops, Playing A Vital Role In Global Agriculture And The Food Industry. However, Its Productivity Is Significantly Threatened By Various Leaf Diseases Such As Leaf Spot, Mosaic, And Curl, Which Lead To Reduced Yield And Economic Losses. Early And Accurate Identification Of These Diseases Is Essential For Effective Crop Management And Disease Control. Traditional Diagnostic Methods, Which Rely On Manual Field Inspection, Are Often Time-consuming, Labor-intensive, And Prone To Human Error. To Overcome These Limitations, This Study Proposes An Automated Chili Leaf Classification System Using Deep Learning Techniques. Convolutional Neural Networks (CNNs) And Transfer Learning Models Are Employed To Automatically Extract Discriminative Features From Chili Leaf Images, Enabling Precise Classification Of Healthy And Diseased Samples. The Proposed Approach Demonstrates High Accuracy And Robustness Compared To Conventional Machine Learning Methods, Making It A Scalable And Efficient Solution For Real-time Disease Monitoring. The Findings Of This Study Highlight The Potential Of Deep Learning–based Classification Systems To Support Farmers And Agricultural Stakeholders In Improving Chili Crop Productivity Through Timely Disease Detection And Management.