This study focuses on the application of Convolutional Neural Networks (CNNs) for the detection and classification of diseases in tomato leaves, a critical issue affecting agricultural productivity. Leveraging a dataset of labeled leaf images, we employed a CNN architecture to automate the diagnosis of various diseases such as early blight, late blight, and bacterial spot. The model was trained on a diverse set of augmented images to enhance its robustness and generalization capabilities. Performance metrics, including accuracy and loss, were evaluated on a separate test set, demonstrating the model's efficacy in accurately identifying disease types. The findings indicate that CNNs can significantly aid farmers and agricultural professionals in early disease detection, facilitating timely interventions and improved crop management. This research underscores the potential of deep learning techniques in precision agriculture, paving the way for future advancements in plant disease diagnostics.