Plant viruses are a global threat to vegetable production, especially for members of Cucurbitaceae & Solanaceae. For decades, the preliminary diagnosis of plant viruses has been based on visual surveys, which many times have often proven to be inaccurate. Whereas laboratory-based detection and diagnosis are time-consuming and labor-intensive. Efficient plant disease forecasting and precise, as well as early disease diagnosis, are inextricably linked. Artificial Intelligence (AI) and Machine Learning (ML) have blended smoothly into the agriculture sector, bringing revolution in Phytopathology. ML, a branch of AI, has enabled precise diagnosis and detection of plant viral diseases at an early stage. Integration of AI/ML has offered timely plant health monitoring, disease forecasting, and data-driven management in vegetable crops. Various ML models/ applications have been analyzed across various vegetables like cucumber, sweet potato, tomato, pepper, cassava, etc., with higher precision. Numerous plant viruses, viz. cucumber mosaic virus, cassava mosaic virus, tomato spotted wilt virus, tobacco mosaic virus, tomato leaf curl virus, groundnut bud necrosis virus, etc. have been detected with >90% accuracy using various ML models like convolutional neural networks (CNNs), Support Vector Machine (SVM), Successive projections algorithm (SPA) with extreme learning machine (ELM), etc. ML models can be coupled with imaging sensors like hyperspectral sensors to increase accuracy and the scope of detection. ML models can also be used to predict disease outbreaks based on the trends of weather and crop data. Therefore, our chapter emphasizes how AI/ML-led applications are strengthening next-generation plant virology.