Understanding genotype × environment (G × E) interactions is a critical component of plant breeding programs aimed at developing high-yielding, stable, and climate-resilient crop varieties. Multivariate statistical models have become essential instruments for unravelling the intricacies of G × E interactions. They enable the recognition of genotypes that demonstrate both extensive adaptability and particular stability across various agro-environmental conditions. By capturing the interactive effects of genotypic and environmental variables, multivariate approaches provide a robust framework for evaluating genotype performance, ranking stability, and identifying mega-environments. Additionally, they allow breeders to assess the influence of specific environmental covariates, thereby informing region-specific selection strategies. Integration of multivariate models with genomic tools such as genome-wide association studies (GWAS), high-throughput phenotyping, and genomic selection (GS) further enhances their predictive power. These approaches help uncover the genetic architecture underlying yield stability and adaptability by linking genomic regions to phenotypic plasticity. Moreover, incorporating omics technologies (transcriptomics, proteomics, metabolomics) with multivariate analysis enables a systems-level understanding of stress-responsive pathways that drive stable performance under adverse conditions. The synergy of multivariate modelling and next-generation sequencing accelerates the finding of molecular markers linked with key traits, improving the efficiency of marker-assisted selection (MAS). As climate variability intensifies, multivariate models will play a crucial role in developing resilient cultivars by enabling more accurate predictions of genotype performance across environments. Their integration into molecular breeding platforms offers a transformative approach to sustainable crop improvement in the context of global environmental challenges.