The research aims to use Convolutional Neural Networks (CNNs) to create an automated system for identifying and categorizing tomato leaf diseases in order to increase agricultural productivity and crop management. By addressing the inefficiencies of traditional manual inspection methods, this study aims to provide timely and accurate disease diagnoses, ultimately benefiting farmers. The methodology involves several key steps, including data collection from high- resolution images of tomato leaves, data preprocessing, and the implementation of CNNs for feature extraction and classification. The model achieved impressive accuracy rates in identifying various diseases, demonstrating the effectiveness of deep learning in agricultural applications. Additionally, the results highlight the robustness of the proposed system against variations in image quality and conditions. This research contributes to the ongoing efforts to improve disease management practices in agriculture. Future work will focus on expanding the model's capabilities to include other plant species and integrating real-time monitoring solutions for enhanced field application.