The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agricultural industrially detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. The Prognosis of Disease in the Mango Fruit Crop IoT and machine learning are used in a complex warning system. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield. The field sensors collected current weather information to detect disease immediately. A regression method that makes use of a random forest is the random forest regression. This work investigates machine learning (ML) methods for identifying and categorising illnesses in mango plants. In this paper, the effectiveness of ML-based classification models for mango crops, as well as their datasets and feature extraction methods, are assessed. Finally, many challenges related to identifying plant diseases are examined