The chapter provides an overview of the escalating problem of antimicrobial resistance (AMR) and the promising potential of artificial intelligence (AI) and machine learning (ML) in addressing AMR. As a threat to global health, AMR reduces the effectiveness of the drugs we already have (despite established AMR definitions and guidelines) and challenges treatment outcomes. Traditional diagnostics were effective, but they are also relatively time consuming, resource intensive, and slow down the clinical decision-making process. We demonstrated how AI and ML approaches are speeding up microbiology diagnostics, predicting drug resistance, and personalizing drug treatments by allowing rapid, accurate, and most importantly, cost-effective solutions. The chapter presented several applications of AI and ML to AMR including identification of bacteria, detection of resistance genes, and predictive analytics to optimize treatment strategies while also providing a sampling of the foundational algorithmic and computational models that have underpinned these advances. We highlighted two key elements that enabled these advances: leveraging big data and bioinformatics, and integrating clinical datasets to provide predictive analytics applicable to the field of microbiology. Finally, there was critical reflection upon the challenges, limitations, and ethical considerations of applying AI in the field of clinical healthcare along with future research and policy possibilities. All of this information becomes part of the body of knowledge as we show how AI and ML can enhance the global response to AMR and improve the clinical outcomes for patients.