To meet the market demand for high-quality products, researchers and manufacturers have invested in the development of accurate methods for estimating shelf-life. Tests that consider the simultaneous effects of different parameters on food degradation are useful tools in shelf-life studies, as these parameters can directly influence quality and safety. With this in mind, the objective of this review is to gather pertinent information from recent studies (2006-2022) pertaining to multivariate analysis applied in accelerated shelf-life tests in order to facilitate a comprehensive understanding. The review focuses on multivariate techniques commonly employed in accelerated shelf-life modeling, namely, principal components analysis, partial least squares regression, orthogonal projections to latent structures discriminant analysis, and hierarchical cluster analysis. Through an extensive literature review, the collected data represent the evolution of these methods, taking into account current trends, advances in food shelf-life techniques, and future perspectives. It was observed that the recent literature provides limited information on the determination of shelf-life under multiple accelerated factors. However, the studies analyzed showed that multivariate analysis can be a useful tool in the interpretation of quality characteristics and can accurately predict the shelf-life of foods compared to univariate kinetic procedures. Multivariate statistical methods addressed in this work are presented as a promising method for foods tested, being applied together with different chemometric techniques. This comprehensive review contributes to the body of knowledge surrounding accelerated shelf-life testing, offering valuable insights for researchers, manufacturers, and stakeholders in the food industry.