Machine learning methods for discovering diseases in plants have acquired much attention reflected in the idea that these methods could change dramatically how people produce crops. This abstract concerns the status quo methodology that uses artificial intelligence to automate disease detection. It provides an overview of its advances and effectiveness in this particular process. Scientists have effectively applied several machine learning methods such as convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and ensemble methods aiming at precisely classifying or diagnosing plant species, which include photos or images and sensor information and spectral data. This chapter examines and compares these methods of managing virtual currency, highlighting their strengths and weaknesses and their application in real-world situations. For example, we can measure the accuracy, sensitivity, and specificity of the various methods used, which generally will show us how effective one is. To this extent, the abstract will mention the importance of the process of data preprocessing, feature extraction, model optimization, and transfer learning in the development of machine learning models with improved robustness and generalization. Results suggest that sophisticated machine learning tools may provide disease management strategies concerning agriculture, which will result in generating positive outcomes in crop health, productivity, and sustainability.