Tomato crops are very useful for our world, not just for many diets but also in the agricultural industry. Countries like Turkey are famous for quality tomatoes, which are used in the majority of dishes. Still, growing tomatoes is a difficult issue. The first reason is the diseases that affect the tomato plants and hence reduce the yield. Advanced deep learning techniques suggest that a novel technology is used to develop a system that can detect and categorize most of the three common diseases occurring to the tomato plants. In this work, the authors consider the use of the state-of-the-art technology, YOLOv8.2.0, which is a new release model, for the detection and categorization of the three most common diseases in tomato plants; this dataset solution worked with images and a dimension for tomato plants, which are infected by most common diseases. The achieved results are promising, in that the model achieved high accuracy for diseases such as blossom end rot, splitting, and sunscald disease. The accuracies are of 80.20%, 39.40%, and 70.10%, respectively, with an overall accuracy of 89.3% for this disease. It would, therefore, set the foundation of this research for an advanced system to be able to detect most of the diseases, with which crop yields—particular in Turkey—would be improved. Tomato crops are of tremendous importance in developing food security and economies worldwide; hence, they need to be protected from the diseases. Effective management of the diseases in question is still, nonetheless, complicated by technological challenges. This research makes a contribution through deep learning and advanced algorithms in the development of better disease detection systems-all the more indispensable in order to ensure tomato crop productivity and maintain a steady food supply.