Machine learning-powered multi-omics for food microbiology and smarter food safety

Innovations in high-throughput omics technologies have not only expanded the capabilities of individual omics platforms but have also catalyzed a shift toward multi-omics integration, enabling a more holistic understanding of microbial dynamics in food safety context. By combining data from various omics layers, researchers can elucidate complex biological pathways of microbial interactions with foods, uncover mechanisms underlying foodborne hazards, and identify predictive biomarkers. These insights, powered by diverse machine learning (ML) algorithms, support the development of smarter food safety interventions. However, challenges related to data quality, standardization, and integration tools still limit the full potential of multi-omics approaches. Despite these obstacles, ongoing advances are steadily enhancing the feasibility and accessibility of multi-omics integration. This progress is reflected in the growing number of food safety studies leveraging novel and innovative ML models to extract actionable biological insights. This review explores the synergistic convergence of multi-omics and ML, highlighting current applications, such as pattern detection, traceability, antimicrobial resistance profiling, and underscores its transformative potential in the future of food safety and microbiology.