Machine learning-based predictive modeling of foodborne pathogens and antimicrobial resistance in food microbiomes using omics techniques: A systematic review.

The globalization of food systems has heightened the risk of foodborne pathogens such as Salmonella, Listeria monocytogenes, and Campylobacter, exacerbated by rising antimicrobial resistance (AMR). Traditional pathogen identification and AMR risk surveillance methods are often labor-intensive and low-throughput, while single-omics approaches fail to capture microbial complexity. Moreover, reliance on individual machine learning (ML) models limits predictive robustness, posing challenges to food safety and public health. This systematic review evaluates ML-based predictive modeling integrated with omics techniques (genomics, metagenomics, and transcriptomics) for foodborne pathogen and AMR risk surveillance. Following PRISMA guidelines, 1245 articles from PubMed, Scopus, and other databases (2015-2025) were screened, selecting 13 relevant studies. These studies applied ML algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM), to enhance predictive accuracy. The selected studies demonstrated predictive accuracies up to 99 % and AUROC scores above 0.90. Key discoveries include genetic markers for Salmonella virulence, Listeria attribution to fruits and dairy, and 145 mobile antimicrobial resistance genes (ARGs) in poultry. Despite these advancements, limitations such as small sample sizes, inconsistent metadata, overfitting, and computational scalability hinder real-world implementation. This review underscores the potential of ML-driven omics frameworks to revolutionize foodborne pathogen and AMR risk monitoring, paving the way for smarter, more resilient food safety systems. However, methodological inconsistencies necessitate standardized protocols, larger datasets, and explainable AI (XAI) to improve reliability and applicability in global food safety monitoring.