Tomato is one of the finest vegetable which is famous all around the world most of the farmers cultivate the potatoes all around the world and export them as goods. Tomato have a great finite taste which can be a best part of side dish to food item. The Tomatoes are roots with lot if minerals hidden in them but all this nutrients can be lost with the attack of pests which will spoil the crop. The leaf are the main part of the plant for the growth without proper leaves the plant cannot be grown for the further nutrients. Tomatoes are a cornerstone of agricultural production, with farmers cultivating and exporting them as key commodities due to their rich flavor and versatile culinary applications. Despite their nutritional value, tomatoes are vulnerable to pests, particularly leaf diseases, which jeopardize crop health and yield. Early disease detection is critical for mitigating losses and ensuring sustainable agricultural practices. To address the imperative of timely disease detection, this study employs the cutting- edge YOLOv8 (You Only Look Once) algorithm, a Deep Learning model built on basis of (CNNs). The most known alternaria is a leaf disease in Tomato called as early blight and the late blight disease, leaf mold, sepotria leaf spot, bacterial spot. Detection of diseases in early stage of the crop field with this project can decrease the loss and early detection of the leaf affected and try to change and adapt to chemicals used by farmers. The YOLOv8, an improved version of the YOLOv5, stands out for its superior performance, particularly in the segmentation of classified datasets for object detection tasks. This study asserts that YOLOv8 outperforms existing methodologies in disease detection, demonstrating increased accuracy. By detecting diseased tomato leaves at their prior stage, this algorithm will farmers to effectively adapt and implement targeted chemical interventions, reducing disease's impact on crop quality as well as yield. Therefore, we use a Deep Learning algorithm YOU ONLY LOOK ONCE is used for Tomato Leaves Plant Disease for Identification Software using Convolutional Network. The convolutional neural network are used for mainly image classification for predicting the best accuracy 0f 98%. The YOLOv8 is new version of YOLOv5, the YOLOv8 mostly used in segmentation of the classified dataset for the object detection required tasks. So, YOLOv8 performs better than the existing methods as its accuracy is high.