With an average annual intake of almost 20 kilograms per person, tomatoes are the most consumed vegetable worldwide. Diseases brought on by dangerous organisms are among the most important factors adversely affecting tomato production's output and quality. Depending on the climate and environmental conditions, tomatoes can be afflicted by a variety of illnesses throughout the planting and growing phases. It is essential for tomato growers to identify possible infections and take the appropriate preventative measures. Applications of artificial intelligence have grown in popularity recently. AI is being used in agriculture to identify plant illnesses. This research uses deep learning, a branch of artificial intelligence, to categories common tomato diseases. In the beginning, samples of frequently seen tomato illnesses were gathered from tomato growers in Kirkuk. Once there were enough data, the system developed with image processing algorithms produced meaningful images. Using a CNN-based GoogleNet deep learning system, the resulting dataset was trained and diseases were classified. The results show that the deep learning system that was constructed has a high degree of success and dependability when it comes to tomato disease classification.