Genotype × environment (G × E) interaction plays a crucial role in agricultural research, particularly in plant breeding, where selecting stable and high-performing genotypes across multiple environments is a key objective. Multivariate statistical methods provide powerful tools to dissect these interactions and improve genotype selection. This chapter presents a comparative analysis of widely used multivariate approaches, including cluster analysis, principal component analysis (PCA), canonical correlation analysis (CCA), multivariate analysis of variance (MANOVA), additive main effects and multiplicative interaction (AMMI) model, and genotype + genotype-by-environment interaction (GGE) biplot, along with newer methods such as the multi-trait genotype-ideotype distance index (MGIDI) and multi-trait stability index (MTSI). The strengths, limitations, and suitability of these methods in handling G × E interactions are evaluated based on interpretability, computational efficiency, and ability to model complex interactions. Traditional methods like AMMI and GGE biplots remain fundamental for visualizing interactions, whereas MGIDI and MTSI provide advanced multi-trait selection strategies. The findings emphasize the importance of selecting appropriate statistical tools based on breeding objectives and dataset complexity to enhance genetic improvement programs.