Plants play an important role in everyone's lives, and they have a significant impact on a healthy environment. This paper describes a method for distinguishing plant leaf infection and a methodology for locating illnesses with caution. The objective is to analyze the sickness of leaves using various machine learning algorithms, such as Support Vector Machine (SVM) and the K-means algorithm. The plant's growth depends on the development of the leaves, which are affected by various illnesses that have been found in recent days. Researchers have started developing various algorithms for leaf disease detection and prevention. This research primarily focuses on the detection of leaf diseases in the following plant species: apple, cherry, grapes, orange, peach, potato, tomato, maize. These diseases include: apple-apple scab, black rot, and cedar apple rust; cherry-powdery mildew; maize-cercospora leaf spot, common rust and northern leaf blight; grape-black rot, esca and leaf blight; peach-black spot; potato-late blight; orange-haunglongbing; tomato-early blight, late blight, leaf mold, and mosaic virus. Its purpose is to categories the name of the leaf-affecting sickness. It is intended to classify the name of the disease that affects the leaf. Also, the percentage of damage due to the leaf disease will be predicted. Various performance metrics, including accuracy, precision, and recall, will be used in the implementation, which will be carried out via MATLAB.