Healthcare-associated infections (HAI) are common adverse events and surveillance is considered a core component of effective HAI reduction programs. Recently, efforts have focused on automating the traditional manual surveillance process by utilizing data from electronic health record (EHR) systems. Using EHR data for automated surveillance, algorithms have been developed to identify patients with ventilator-associated pneumonia and bloodstream, surgical site infections, urinary tract, and Clostridioides difficile infections (sensitivity 54.2%–100%, specificity 63.5%–100%). Methods based on natural language processing have been applied to extract information from unstructured clinical information. Further developments in artificial intelligence (AI), such as large language models, are expected to support and improve a variety of aspects within the surveillance process; for example, more precise identification of patients with HAI. However, AI-based methods have been applied less frequently in automated surveillance and more frequently for early prediction, particularly for sepsis. Despite heterogeneity in settings, populations, sepsis definitions, and model designs, AI models have shown promising results, with moderate to very good performance (accuracy 61–99%) and predicted sepsis within 0–40 hours before onset. AI-based prediction models that can detect patients at risk of developing different HAI should be explored further. The continuous evolution of AI and automation will transform HAI surveillance and prediction, offering more objective and timely infection rates and predictions. The implementation of AI-supported automated surveillance and prediction systems for HAI in daily practice remains scarce. The successful development and implementation of these systems demand requirements related to technical capabilities, governance, practical and regulatory considerations, and quality monitoring.