Agriculture is the fundamental basis of India’s economy, with almost 65% of the people relying on farming. Tomatoes, which are extensively grown vegetables, play a crucial role in Indian families. However, they are particularly vulnerable to several illnesses like late blight, septoria leaf spot, leaf mold, bacterial spot, and infestations by two-spotted spider mites. Prompt detection of these diseases is essential for preserving crop vitality, maximizing productivity. This research introduces an enhanced method for detecting diseases in tomato plants at an early stage. The method utilizes a Convolutional Neural Network (CNN) in conjunction with the You Only Look Once Version 8 (YOLO v8) object identification model. Our model overcomes the problem of under-fitting in prior approaches by using an extensive dataset of tomato leaf pictures. The proposed methodology attained a 96% accuracy rate, showcasing its capacity to aid farmers in promptly detecting diseases, thereby diminishing crop losses and improving agricultural productivity as a whole.