Vol. 9, Special Issue 1, Part N (2025)

Global climate change is imposing unprecedented and escalating threats to agricultural productivity, jeopardizing global food security. The slow pace of traditional plant breeding is inadequate to develop crop varieties with the necessary resilience to novel environmental challenges, such as increased frequency of drought, extreme heat events, and shifting pathogen landscapes. A paradigm shift is underway, moving from conventional selection methods to a new, accelerated, and data-driven approach to crop improvement. This review focuses on the convergence of three transformative pillars that are central to this new paradigm: Speed Breeding, Genomic Prediction, and Machine Learning. Speed Breeding protocols, which manipulate photoperiod and temperature to shorten generation times, allow breeders to cycle through multiple generations per year, drastically reducing the breeding cycle length. Genomic Prediction utilizes whole-genome marker data to forecast the genetic merit of individuals, enabling accurate selection at early developmental stages and further accelerating genetic gain. Machine Learning provides a powerful suite of computational tools to enhance the accuracy of genomic predictions by capturing complex, non-linear genetic effects and to extract meaningful biological insights from the massive, high-dimensional datasets generated by modern high-throughput phenotyping platforms. We dissect the methodologies of each component, emphasizing how their integration creates a powerful, synergistic pipeline for developing "climate-smart" crops. Speed Breeding provides the engine for rapid population development, while Genomic Prediction acts as the navigation system, guiding selection decisions. Machine Learning serves as the central processing unit, integrating vast multi-omic and environmental data to build robust predictive models that can forecast crop performance in future climate scenarios. We explore advanced models that incorporate genotype-by-environment (GxE) interactions, the application of deep learning for image-based phenotyping, and the potential of these integrated strategies to design and deploy climate-resilient varieties with unprecedented speed and precision. Finally, we address the significant challenges, including the management of "big data," the need for enhanced computational infrastructure, and the "black box" nature of some algorithms, while providing a forward-looking perspective on the future of digitally-enabled, accelerated plant breeding.