<p>Antimicrobial resistance (AMR) continues to undermine the effectiveness of modern medicine, with hospital-acquired infections (HAIs) as major contributors to morbidity and mortality. Traditional surveillance systems for AMR in HAIs are often fragmented, delayed, and reactive, limiting their ability to inform timely interventions. Emerging evidence demonstrates that artificial intelligence (AI) can transform AMR surveillance and control. AI can enable predictive modelling, risk stratification, and outbreak forecasting. This narrative review describes various AI applications for monitoring and evaluating antimicrobial resistance (AMR) among HAIs in hospital settings. We begin by discussing how machine learning models can predict the emergence and spread of antibiotic-resistant pathogens. This is achieved through the analysis of various datasets, including microbiological results, electronic health records, and hospital workflows. Patient-level risk scoring systems are examined, demonstrating their ability to identify individuals at increased risk of multidrug-resistant infections. We describe AI-powered early-warning systems that provide outbreak alerts, enabling timely infection prevention and control measures. Hospital applications, including predictive resistance models validated in European hospitals, such as those in Lower Saxony, Germany, and early-warning dashboards tested in Asia and Africa, illustrate the potential impact of these approaches. However, these studies vary substantially in their designs, validation methods, and implementation contexts. To ensure sustainability, we propose a roadmap for integrating AI into AMR programs. We discuss ethical, legal, and regulatory frameworks. We also discuss strategies for capacity building, cost-effectiveness, and scalability, particularly in low- and middle-income countries. Our findings suggest that AI has the potential to strengthen infection control, enhance stewardship, and provide resilient defences within hospital environments.</p>