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Cotton Is One Of The Most Important Commercial Crops Worldwide, And Its Productivity Is Severely Affected By Various Leaf Diseases Such As Bacterial Blight, Grey Mildew, Fusarium Wilt, And Alternaria Leaf Spot. Early And Accurate Detection Of These Diseases Is Essential For Minimizing Yield Losses And Ensuring Sustainable Crop Management. Traditional Diagnostic Methods Relying On Manual Observation Are Often Time-consuming, Subjective, And Prone To Human Error, Making Them Unsuitable For Large-scale Monitoring. In Recent Years, Advancements In Computer Vision And Machine Learning Have Enabled The Development Of Automated Systems For Disease Detection And Classification. This Survey Provides A Comprehensive Overview Of Existing Techniques For Cotton Leaf Disease Identification, Ranging From Traditional Image Processing Methods To Deep Learning-based Approaches. Various Algorithms Such As Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), Random Forests, And Transfer Learning Models Are Discussed In Terms Of Their Accuracy, Robustness, And Computational Efficiency. Furthermore, The Survey Highlights Publicly Available Datasets, Key Performance Metrics, Challenges Such As Variations In Illumination And Background Noise, And Potential Solutions Through Hybrid And Ensemble Models. Finally, It Outlines Future Research Directions, Emphasizing The Need For Lightweight, Real-time, And Scalable Solutions Deployable In Smart Farming Applications.