Machine learning models have been tested in the current research for detecting and classifying tomato diseases at their early stages. This is one of the core tasks of modern agriculture, being extremely beneficial in maintaining the health and, accordingly, the production of tomatoes. In order to achieve this, a dataset of 3450 images was totaled, containing both the diseased and healthy leaves of tomato. The dataset was split with a 70% portion for training and the rest 30% for testing. Therefore, the current research seeks to train and evaluate the performance of several machine learning models, including Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and VGG-16 architecture; the results showed that the CNN model had the highest measure over the precision, recall, F1 score, and accuracy that averaged over 96.8%, 96.5%, 96.6%, and 96.77%, respectively. The KNN model showed particularly high performance over the metrics of interest with precision, recall, F1 score, and accuracy of 94.3%, 94.0%, 94.1%, and 94.32%, respectively. The RNN model depicted some excellent indicators that pointed to the ability to catch the sequential dependencies with a precision, recall, F1 score, and accuracy of 88.2%, 83.5%, 85.7%, and 85.60%, respectively, which was still lower than CNN and KNN. Finally, the VGG-16 architecture showed quite competitive results over the precision, recall, F1 score, and accuracy—91.7%, 93.2%, 92.4%, and 92.20%, respectively. Therefore, it is evident that the current research provided clear evidence that the employment of ML was effective as pertaining to the accurate detection and classification of the tomato diseases with the CNN model displaying robustness and being highly potential.