This review aims to provide an in-depth evaluation of various deep learning approaches used for the detection and classification of tomato leaf diseases, a critical challenge in smart agriculture. By examining a wide range of methods, the study seeks to identify the most effective deep learning models in terms of accuracy, speed, and robustness in real-world scenarios. Special emphasis is placed on both qualitative and quantitative performance analysis, including metrics such as precision, recall, and F1-score, to assess the effectiveness and reliability of the techniques. Image segmentation, including Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN), plays a pivotal role in isolating and identifying diseased areas on leaves. Furthermore, the classification aspect is assessed to determine which methods offer the best differentiation between disease types. The review also highlights the technical challenges associated with deploying these models in diverse agricultural settings, such as variations in lighting, image capture angles, and image quality. The ultimate goal is to guide future research and technological advancements towards more efficient solutions for early disease detection, reducing agricultural losses, and enhancing large-scale tomato crop management.