Tomato plant diseases significantly impact global food production by lowering crop output and affecting economic resilience. This research introduces a hybrid deep learning approach that merges Convolutional Neural Networks (CNNs) for analyzing spatial features with Long Short-Term Memory (LSTM) networks to capture changes in environmental conditions over time. This integration boosts the accuracy of disease classification and delivers practical guidance for agricultural practices. Nonetheless, the approach faces hurdles such as unbalanced datasets, fluctuating environmental factors, and the high computational cost of deep learning—especially in areas with limited resources. Future studies will aim to improve processing efficiency, advance data augmentation methods, and adapt the model for use with various crops.