Agriculture is one of the fastest growing sectors across the entire world which includes various types of fruits, vegetables, crops, etc. Mango production increases gradually year by year, at the same time, the spread of diseases also increases due to various parameters like insects, climate change, etc. In particular, Krishnagiri district in Tamil Nadu produces 300,000 tons of mangoes per year. Mango fruit defect affects the quality as well as the nation’s economic growth and productivity. The proposed work mainly focuses on mango fruits and the dataset used here consists of various diseases such as Alternaria, Anthracnose, Black mold Rot, Stem Rot, and finally healthy fruit. The dataset has been pre-processed with various techniques, then it is been applied with a convolutional neural network, especially Alex Net for classification. The proposed work on mango fruit is to predict whether the fruit is healthy or defective thereby developing a webpage that displays the type of diseases and their respective remedies for the predicted diseases when we upload an image. The results demonstrate that the proposed AlexNet-based model achieves high accuracy in classifying mango fruit diseases, effectively identifying defects, and providing actionable insights through the user-friendly webpage interface.