The study aims to use Convolutional Neural Networks (CNNs) to develop an automated system for identifying and categorizing tomato leaf diseases, with the goal of increasing agricultural productivity and improving crop management. By addressing the inefficiencies of traditional manual inspection methods, this research 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 demonstrated effectiveness in identifying various diseases, showcasing the potential of deep learning in agricultural applications. Moreover, the system is robust against variations in image quality and environmental conditions. This research contributes to 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 applications.