Mangoes are valued both nutritionally and economically. However, Mango Leaf Disease presents a significant challenge for agricultural practitioners. Effective disease identification is crucial for addressing this issue, which requires extensive knowledge of plant diseases. Recent advancements in deep learning have significantly enhanced the ability to identify and classify plant diseases. The detection of illnesses in mango leaves using several machine learning and deep learning algorithms is covered in this research. The objective is to precisely detect and classify various diseases affecting mango leaves. The dataset is evaluated using several ML algorithms, including SVM, Decision Trees, Random Forest, Gradient Boosting, and KNN, as well as deep learning models such as CNN with sequential models, VGG16, VGG19, and YOLO. The proposed system leverages the latest versions of YOLO, specifically YOLOv8, YOLOv9, and YOLOv10 for the identification of mango leaf diseases. The models are trained on a custom dataset comprising eight classes. Among the various evaluated, the YOLOv8 and YOLOv9 models demonstrated significantly superior performance compared to other models with an accuracy of 98.83% and 99.16% respectively, highlighting their effectiveness in mango leaf disease detection. The model is designed to enable users to detect and classify diseases accurately without requiring expert intervention.