The increasing frequency and intensity of plant diseases under changing climate conditions pose a significant threat to global agriculture. AI under climate change scenarios and disease prediction through forewarning model plays an important role for the emerging plant disease prediction is crucial for protecting global food security. By assimilating developed AI techniques with climate data, these models can preciously predict disease eruptions, allowing for timely intersession can cause minimization of crop losses in an environment that is becoming more unpredictable. Using information from remote sensing IoT sensors, high resolution satellite image, climate data and pathogen surveillance, real-time plant disease prediction makes use of AI and forewarning models. By integrating real-time environmental parameters, this offers continuous, large-scale data collecting for more dynamic and precise forecasts which is a stark contrast to traditional methods. AI methods such as machine learning and neutral networks, examine large datasets that include historical disease occurrences, soil health and climate. As an example, AI-powered wheat rust prediction models, when compared to conventional techniques, reduce crop losses by 30% and reach 85–95% accuracy worldwide. This AI- and forewarning model-driven approach is transforming plant disease prediction, particularly crucial under changing climate conditions. Organizations like IFPRI, FAO, MANAGE and the World Bank actively promote AI-based forewarning models to improve farmer welfare and agricultural development. The combined power of AI and forewarning models creates robust, adaptable, predictive systems that safeguard crops from emerging diseases, supporting sustainable agriculture and global food security.