Wheat plays a critical role in global food security. However, its productivity and quality are increasingly threatened by biotic and abiotic stresses driven by climate variability, pathogen evolution, and soil degradation. Conventional breeding and management approaches are constrained by slow data acquisition, subjective field evaluations, and delayed responses. Recent advances in artificial intelligence (AI), encompassing machine learning, deep learning, computer vision, and advanced sensor networks, offer transformative opportunities for stress detection, prediction, and mitigation in wheat systems. AI-driven platforms facilitate high-throughput phenotyping, real-time monitoring of disease and pests, early prediction of climate-induced stress, and accelerated genomic analysis, collectively advancing wheat improvement and precise postharvest management. Progress in modelling major biotic threats, including rust, powdery mildew, viral diseases, and insect pests, as well as abiotic challenges such as drought, heat, salinity, and nutrient deficiencies, highlights AI’s capacity to integrate diverse datasets into robust, scalable decision-support tools. Despite ongoing challenges related to data inconsistency, model interpretability, infrastructure limitations, and restricted field deployment, the outlook for AI-enabled wheat improvement in managing multiple stresses remains optimistic. Emerging innovations, such as large-scale foundation models, digital twins, integration of Internet of Things (IoT) with AI, and AI-assisted gene editing, are poised to enhance wheat resilience, promote long-term sustainability, and ensure food security.