Wheat is a cornerstone of global food security with about 799 million tonnes/year of production, serving as a staple food for 40% of the world’s population. However, with the global population projected to reach 9.8 billion by 2050, agricultural production must increase by 70–100% to meet rising food demands. Wheat cultivation faces challenges from climate change, population growth, and the need for sustainable practices, necessitating innovative solutions to enhance yield, resilience, and quality. Artificial intelligence (AI has emerged as a transformative tool in addressing these challenges. By leveraging machine learning, deep learning, and other AI-driven technologies, agriculture is witnessing advancements in yield prediction, pest and disease management, and climate adaptation. AI models, such as artificial neural networks and random forest regression, have demonstrated superior accuracy in forecasting wheat yield. Additionally, AI facilitates precision agriculture through real-time monitoring, optimizing resource use, and improving crop health. In breeding, AI accelerates the development of stress-tolerant wheat varieties by integrating genomic, phenotypic, and environmental data, significantly reducing the time required for traditional breeding methods. Despite its potential, the application of AI in agriculture faces challenges, including the need for extensive datasets, computational resources, and skilled personnel. Nonetheless, AI-driven innovations hold immense promise for enhancing wheat productivity, resilience, and sustainability, ensuring food security in the face of climate change and a growing global population. This chapter explores the role of AI in wheat cultivation, focusing on its applications in yield prediction, pest and disease management, and climate adaptation, while addressing challenges for sustainable agriculture.