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Mango Is One Of The Most Economically Significant Fruit Crops Cultivated Worldwide, But Its Yield And Quality Are Severely Affected By Various Leaf Diseases Such As Anthracnose, Powdery Mildew, And Bacterial Canker. Accurate And Timely Detection Of These Diseases Is Crucial For Effective Crop Management, Prevention Of Yield Loss, And Ensuring Sustainable Agricultural Practices. Traditional Disease Identification Methods Relying On Visual Inspection Are Labor-intensive, Time-consuming, And Prone To Human Error. In Recent Years, Numerous Computational Techniques, Ranging From Conventional Image Processing And Machine Learning To Advanced Deep Learning Models, Have Been Developed To Improve The Efficiency And Accuracy Of Mango Leaf Disease Detection. This Study Presents A Systematic Analysis Of Existing Techniques Used For Mango Leaf Disease Detection, Highlighting Their Methodologies, Strengths, Limitations, And Application Scope. The Comparative Review Emphasizes The Transition From Handcrafted Feature-based Approaches To Data-driven Deep Learning Architectures, Which Have Demonstrated Superior Performance In Terms Of Robustness And Scalability. Furthermore, The Analysis Discusses Challenges Such As Variability In Imaging Conditions, Dataset Limitations, And Computational Complexity, While Also Outlining Future Research Directions For Developing More Generalized, Real-time, And Farmer-friendly Detection Systems.