Artificial Intelligence (AI) has emerged as a powerful tool in healthcare, particularly in predicting and managing outbreaks of healthcare-associated infections (HAIs). This review explores the role of AI technologies, including machine learning, neural networks, and natural language processing, in enhancing infection control strategies. AI offers several advantages over traditional methods, such as increased accuracy and speed in analyzing complex datasets from electronic health records (EHRs), genomic data, and environmental factors. The integration of AI in predicting HAIs has been demonstrated through various case studies, showcasing its potential to improve patient outcomes and reduce healthcare costs. However, the adoption of AI in clinical settings faces challenges, including data quality issues, privacy concerns, and integration with existing hospital systems. This review also highlights future directions for AI in healthcare, emphasizing the need for more research to optimize AI models and address ethical considerations. The findings suggest that AI can significantly impact infection control practices, offering a promising avenue for enhancing patient safety and public health.