To prevent reductions in agricultural product production and quantity, it is crucial to identify plant diseases. The application of various machine learning and image processing techniques reduces the issues in the agricultural industry. With the aid of various ML and image processing approaches, the major objective of this review is to identify rice plant diseases using picture inputs of Infectious rice plants. Moreover, the key Machine Learning (ML) and image processing concepts for identifying and categorizing plant diseases are covered. k-Nearest Neighbor Classifier (KNN), Support Vector Machine (SVM), Genetic Algorithms (GA), and Probabilistic Neural Networks (PNN) are a few of the classification methods utilized in agricultural research. The quality of a conclusion can vary depending on the input data, so choosing a categorization method is an important responsibility. The categories for plant leaf diseases are used in a variety of sectors, including biology, agriculture, etc. This research presents a thorough analysis of rice plant illnesses, picture dataset size, pre-processing, segmentation methods, and classifiers.