Metagenomics and Machine Learning for Foodborne Pathogen Risk Prediction: Current Status, Challenges, and Future Directions

ABSTRACT

The integration of metagenomics and machine learning (ML) is redefining the landscape of foodborne pathogen risk prediction. By leveraging high‐resolution microbial community data, ML models offer enhanced predictive accuracy and the potential for early warning systems. This review synthesizes recent advancements in this interdisciplinary field, highlighting applications in pathogen detection, antimicrobial resistance surveillance, and source attribution. Critically, unlike prior reviews that focus primarily on technological promise, we systematically evaluate where these approaches fail under real‐world conditions—including poor cross‐study reproducibility, overoptimistic performance claims, and weak biological validation. Despite promising developments, persistent challenges include data heterogeneity, computational demands, model interpretability, and industrial implementation barriers. To move beyond descriptive accounts, we propose a structured framework that prioritizes standardized benchmarking, explainable AI with biological grounding, and collaborative validation strategies—a combination largely absent from existing reviews. This review aims to provide a critical and actionable roadmap for translating metagenomics‐ML innovations into scalable, real‐world food safety solutions.