Tomato is one of the good and productive vegetables, but it has suffered from many diseases. Also tomato leaf has suffered from many diseases. One of the most popular vegetables in the world, tomatoes can suffer from a number of diseases that can significantly reduce their productivity. Effective management and prevention of disease depend on early detection. In order to automate the process of identifying and categorizing common tomato leaf diseases, this paper proposes a tomato leaf disease detection system based on machine learning techniques, including YoloV9. A custom dataset of tomato leaf images is used to train the system, and performance evaluation shows how well the model detects and classifies a variety of diseases, including Septoria Leaf Spot, Early Blight, and Late Blight. YoloV9 is the latest version of deep learning approach. The findings show that machine learning-based systems can greatly increase disease detection’s precision and effectiveness, which will result in improved farming methods. For a better future we need this type of system more and more. Finally early detection of tomato leaf can help to reduce the loss.