Escalating climate variability, compounded by intensifying abiotic stresses, is amplifying genotype-by-environment interaction (G × E) effects and threatening the sustainability of global food systems. The dynamic nature of G × E demands a paradigm shift toward breeding for high and stable yields across diverse, often suboptimal environments. Conventional breeding approaches emphasizing yield maximization under optimal conditions inadequately capture the spatiotemporal complexity of G × E, necessitating a shift toward breeding for yield stability and resilience across diverse and frequently suboptimal environments. This chapter outlines an integrated, G × E-informed breeding framework incorporating target population of environments (TPE) redefinition through genetic correlations and environmental covariates, genomic-assisted selection, enviromics, multi-environment trials (METs), and predictive modeling. Advances in high-throughput precision phenotyping (HTPP) enable field-based quantification of dynamic stress responses, enhancing the linkage of phenotypic, genomic, and environmental data. Evidence from maize and rice programs (e.g., CIMMYT’s climate-resilient breeding strategy; IRRI’s OneRice Framework) demonstrates that genetic gains under both stressed and unstressed conditions can be simultaneously achieved through balanced selection for plasticity, resilience, and high yield potential. Operationalizing such pipelines requires institutional investment in distributed phenotyping networks, data integration platforms, and enabling policy frameworks to sustain long-term adaptive breeding capacity. By integrating modern statistical modeling and application of contemporary systems-based tools—such as crop simulation models (CSMs), AI- and ML-driven predictive frameworks, digital decision support systems (DSS), and leveraging dynamic G × E, modern breeding programs can accelerate genetic gain and deliver cultivars with broad or environment-specific adaptation suited to current and projected climatic regimes.