Antimicrobial Resistance (AMR) in bacteria is a global threat with increased prevalence found in isolates from food animals including those in the United States. This is due to the emergence of new mechanisms giving rise to multi-drug-resistant bacterial strains. In this research project, retrospective Antimicrobial Susceptibility Testing (AST) surveillance datasets for Salmonella and Enterococcus bacteria collected by the Food and Drug Administration (FDA) were utilized to determine the co-occurrence of AMR to different antibiotics. For this purpose, a Bayesian Network was implemented and trained and interesting rules were generated using association rule mining. Whole genomic sequence (WGS) data was also used to detect AMR genes and check for co-occurrence.