Enteric pathogens, particularly bacterial pathogens, are associated with millions of cases of foodborne illness in the U.S. and worldwide, necessitating the identification and development of methods to control and minimize their impact on public health. Predictive modeling and quantitative microbial risk assessment are two such methods that analyze data on microbial behavior, particularly as a response to changes in the food matrix, to predict and control the presence and prevalence of these pathogens in food. However, a number of these bacterial enteric pathogens, including Escherichia coli, Listeria monocytogenes, and Salmonella enterica, have inherent genetic and phenotypic differences among their subtypes and variants. This has led to an increasing reliance on "omics" technologies, including genomics, proteomics, transcriptomics, and metabolomics, to identify and characterize pathogenic microorganisms and their behavior in food systems. With this exponential increase in available data on these enteric pathogens, comes a need for the development of novel strategies to analyze this data. Advanced data analysis/analytics is a means to extract value from these large data sources, and is considered the core of data processing. In the past few years, advanced data analytics methods such as machine learning and artificial intelligence have been increasingly used to extract meaningful, actionable knowledge from these data sources to help mitigate food safety issues caused by enteric pathogens. This chapter reviews the latest in research into the use of advanced data analytics, particularly machine learning, to analyze "omics" data of enteric bacterial pathogens, and identifies potential future uses of these techniques in mitigating the risk of these pathogens on public health.