Rural Development in the country is based on agriculture and GDP of India is largely based on primary sector which is to do with agriculture and agro based industries. One of the major cash crops that India produces is wheat and is a staple food for many communities in the country. This yield in wheat is determined by several factors such as irrigation, technology and modernized agro based developments. One of the issues impacting greater yield in wheat crops is the impact of fungi and pests that are detrimental in production. Fungi such as Septora, flag smut, leaf blight, stripe rust and powdery mildew creates around 15–20% loss for farmers annually in India. The present study is an exploration made combining social science perspectives aimed at identification and classification of diseases as well as the health of the leaves using a recognition system. Using VGG-19 as the feature extractor and employing Otsu’s threshold-based segmentation, the present study is built in a way to determine the regions of interest in each image. Through XG_Boost classifier, the researchers identified and classified various wheat leaf diseases and the resultant model OTS-XGB has got a precision of 96.00% and precision of 97.00%. The recall/sensitive rate is 97% and the F1-score is found to be 96.00%. The model identified will be a valuable instrument for farmers to identify and manage leaf based diseases and ensure there is maximum crop production. By expanding our knowledge of these diseases, we can develop more effective strategies for preventing and treating them, ultimately leading to increased food security and sustainability.