Probiotics are defined as live microorganisms that confer health benefits to the host; nevertheless, their therapeutic potential has been historically constrained by a reliance on phenotypic screening and generalized efficacy claims. This review shifts probiotic research toward a system-level understanding by highlighting the transformative impact of omics technologies and in silico approaches in overcoming these limitations. We examine how integrating genomics, transcriptomics, proteomics, and metabolomics can enable precise identification of strains, elucidate mechanisms of action, and reveal strain-specific functional properties often missed by conventional methodologies. A key focus of this review is the application of computational biology, including genome-scale metabolic models and artificial intelligence, which facilitates the predictive modeling of host–microbe interactions and accelerates the rational design of next-generation probiotics. The review further synthesizes recent insights regarding the role of these technologies in supporting personalized nutrition interventions that are specifically tailored to individual molecular profiles, while placing probiotics within the broader context of fermented foods and microbiota-modulating strategies. Finally, we address critical challenges – including issues related to data standardization, regulatory hurdles, and validation of in silico predictions – that must be resolved to translate these technological advances into safe, effective, and customized clinical applications.