Hospital-acquired infections (HAIs) significantly burden global healthcare systems, exacerbated by antibiotic-resistant bacteria. Traditional infection control measures often lack consistency due to variable human compliance. This comprehensive review aims to explore the role of artificial intelligence (AI) in enhancing infection control and prevention in hospitals. A systematic literature search was conducted using databases such as PubMed, Scopus, and Web of Science up to October 2024, focusing on studies applying AI to infection control. The review synthesizes current applications of AI, including predictive analytics for early detection, automated surveillance systems, personalized medicine approaches, decision support systems, and patient engagement tools. Findings demonstrate that AI effectively predicts HAIs, optimizes antimicrobial use, and improves compliance with infection prevention protocols. However, challenges such as data quality issues, interoperability, ethical concerns, regulatory hurdles, and the need for substantial investment impede widespread adoption. Addressing these challenges is crucial to leverage AI's potential to enhance patient safety and improve overall healthcare quality.