This study introduces an enhanced deep learning approach for accurately classifying tomato ripeness levels using a modified Inception-V3 model, with applications in large-scale agricultural environments. Leveraging a dataset of 7,224 RGB images of tomatoes in varying ripeness stages, the modified model achieved a validation accuracy of 98.42%, with precision, recall, and F1-score values exceeding 98%. These results outperform the base Inception-V3 model and other commonly used architectures such as ResNet and VGG, showcasing the model's superior classification accuracy and computational efficiency. Key modifications include adjustments to filter sizes and the configuration of inception blocks, which significantly reduce the parameter count, thereby optimizing computational resources and enhancing feature extraction for multi-scale image analysis. The model was tested under three lighting conditions, achieving over 97% accuracy across all categories. Confusion matrices validate its effectiveness, highlighting