Early detection and identification of plant diseases are critical—and currently very difficult—for the agriculture industry. Major utilization of AI that aids in our successful achievement of this goal is machine learning. It analyzes and interprets data using a collection of algorithms so that it can be used to learn from and make intelligent judgments. In order to complete this research, we employed a dataset that included pictures of both healthy and sick plant leaves. We then used image processing to derive the features from the photos. Next, we employ various machine learning methods, such as random forest, support vector machine, Naïve Bayes, and others, to model this dataset. The objective is to conduct a comparison analysis to determine which of those algorithms has the highest accuracy in disease prediction. We evaluate various machine learning algorithms’ projection times as well as their precision and error rates. With all of these comparisons, insightful findings for the current endeavor can be drawn.