The incidence of infections caused by extraintestinal Escherichia coli (ExPEC) is rising globally due to their increasing resistance to standard antibiotics. This results in the use of broader-spectrum drugs, prolonged patient ill-health and more nosocomial infections. E. coli sequence type 131 (ST131) is the predominant ExPEC clone worldwide. The antimicrobial resistance (AMR) gene repertoire of ST131 is evolving rapidly due to the widespread use of β-lactam (bla) antibiotics. Here, we performed a genomic investigation of an ST131 outbreak in a long-term care facility (LTCF) to describe transmission, within-host clonal diversity, genetic diversity of antibiotic resistance and the evolution of ST131 in the LTCF over a seven-year period. We analyzed the population structure and inferred the genealogical history of the LTCF isolates in the context of local hospital and global collections of ST131 to elucidate the epidemiology of ST131. We confirmed our initial hypotheses by reconstructing the evolutionary history of a much larger population consisting of >4000 global ST131 genomes This provided a deeper resolution of their evolutionary trajectories and the adaptive mechanisms of AMR driven by their ESBL genes, particularly cefotaximase (blaCTX). We further investigated the intersection of the AMR genes (AMRGs) found in ST131 with that of the human microbiome to understand the extent of their loss, gain and spread across different bacterial species. Across all strains, a large number of ST131’s AMRGs were found in a total of 794 genes in the human microbiome. Various gene families were represented, including transporters, transcription factors, β-lactamases and cell wall biosynthesis enzymes. To establish the main culprit for the dynamic nature of the blaCTX-M genes, we performed long read sequencing using a GridION X5 instrument. Analysis of long read-only assemblies revealed a clear and robust result on the genetic flanking context of blaCTX-M genes in both plasmid and chromosomes. Overall, our findings underpin the tremendous potential power for improving our current treatment of bacterial infections using high-throughput analysis of whole genome sequence data.