Introduction: Medical errors have become an important issue of patient safety in the Kingdom of Saudi Arabia (KSA) as well as in other countries of the world. Although these errors are seen as personal failures, modern safety science recognizes the mistakes as the outcomes of the systemic failures. Infection Prevention and Control (IPC) systems, which are based on either a standardized clinical bundle, or electronic surveillance, are critical elements in the healthcare infrastructure. Since Saudi Arabia is experiencing a massive healthcare repositioning process according to Vision 2030, it is crucial to comprehend how these systems will specifically influence the process of mitigating clinical errors in enhancing the health outcomes of the country.

Study Objective: The goal of this systematic literature review is to assess and integrate the existing research on how infection prevention and control (IPC) systems affect the occurrence and reduction of medical errors in Saudi Arabian hospitals and healthcare facilities.

Methodology: A systematic search was carried out in PubMed, Scopus, Web of Science, and the Saudi Digital Library on peer-reviewed articles published in 2010-2026. The inclusion of studies was based on the requirement to present empirical data on the IPC compliance and medical error rates (medication, diagnostic, or procedural) in KSA. The PRISMA guidelines resulted in the extraction of 20 central studies comprising cross-sectional audits, cohort studies, quality improvement reports, etc. Quality appraisal was conducted in terms of JBI (Joanna Briggs Institute) tools of critical appraisal.

Conclusion: Infection control systems are integrated, which is one of the pillars of reducing errors in Saudi Arabian hospitals. The shift of manual to digital surveillance and implementation of strict national accreditation has provided a more resilient healthcare setting. In a bid to minimize the medical errors further, the future efforts should be on maintaining the IPC compliance beyond the accreditation periods and use AI-driven predictive modeling to stop possible errors on-the-fly.