<p>Climate change-induced drought threatens global food security, with reports indicating that maize yield losses can exceed 30% in vulnerable regions, such as sub-Saharan Africa and South Asia. While traditional approaches struggle to connect genomic insights with field-level solutions, explainable artificial intelligence holds promise for transforming drought-resilience research. This review synthesizes the literature to present a conceptual, maize-centric framework for integrating explainable artificial intelligence across biological scales. We discuss how explainable artificial intelligence techniques, such as Shapley Additive Explanations and attention mechanisms, are being used to identify key drought-response elements, from novel promoter motifs to ABA signaling genes. The integration of these predictions with CRISPR screening is highlighted as a promising validation pathway, demonstrating cross-species applicability. In phenomics, we examine how unmanned aerial vehicle-based hyperspectral imaging, paired with explainable artificial intelligence, achieves high stress classification accuracy in maize, with similar protocols being developed for other cereals. For environmental monitoring, we review how models combining Internet of Things sensors with Long Short-Term Memory networks improve drought forecasts. Furthermore, architectures like the Stress-Detection Network are presented as a template for multi-crop integration. The review also addresses ongoing challenges, including data bias and computational barriers, and explores potential solutions such as federated learning and edge computing. Collectively, the literature suggests that this framework could help accelerate breeding cycles and stabilize yields under drought. These advances position explainable artificial intelligence as a pivotal tool, with maize serving as a model to bridge discovery and application for next-generation crop improvement.</p>