India’s agriculture continues to be the engine of the economy using up to 70% of its population. But even so, diseases of crops, and especially of those which are due to fungi, constitute a serious menace to agricultural productivity. Fungal diseases are especially hard to bear on oilseed crops that are indispensable for edible oils and other products. Current methods of disease detection depend heavily on manual inspection, a process that is time-consuming and susceptible to human error. In this paper, we review the role of emerging technologies, including machine learning (ML), artificial intelligence (AI), computer vision, and the Internet of Things (IoT) in detection and management of fungal diseases in oilseed crops. Data is collected through drones and IoT sensors, image processed using convolutional neural networks (CNNs), and predictive model utilised through application of machine learning algorithms in the framework. Detecting disease as soon as possible helps farmers take action to prevent or reduce losses and increase yields. The purpose of this paper is to discuss the potential of the use of digital technologies in agriculture, particularly with regard to the possibility of using AI-driven systems for fungal disease management and a sustainable agricultural practice.