Background and aims: Hospital infection occurs after hospitalization of the patient in the hospital,which is associated with significant mortality and increases the cost of treatment. Approximately۵ to ۱۰% of patients admitted to the hospital suffer from some type of infection, although۳۰% to ۵۰% of hospital infections are preventable. Applied and advanced artificial intelligencesolutions enable a hospital to be more successful in predicting, controlling and preventing, diagnosingand treating hospital infections. The purpose of this study was to investigate the variousartificial intelligence approaches implemented and their impact on preventive measures, control,diagnosis and treatment of hospital infections.Method: This systematic review study was conducted in December ۲۰۲۲ by searching keywordshospital Infection, artificial Intelligence, deep learning, machine learning, computing methodologies,blockchain, robotic, expert systems, fuzzy logic in the title and abstract in the reliabledatabases of Web of Science, Scopus, PubMed without time limit. The inclusion criteria includingEnglish language were checked by the researchers, systematic review and covid-۱۹ studies wereexcluded. A checklist was provided to extract data including the type of artificial intelligencesolution, the target scope including prediction, prevention and control, diagnosis and treatment ofhospital infection and the most important outcome of the study.Results: A total of ۶۰۰ articles were reviewed, and after reviewing the full text of the articles,۱۰۹ articles were included in the study. In ۷۰ articles (۶۴%) artificial intelligence solutions werein the field of prevention and control of hospital infections. In ۳۴ articles (۳۱%), artificial intelligencesolutions were used to predict hospital infections. In ۵ articles (۵%), artificial intelligencesolutions were presented in the field of diagnosing hospital infections. In ۷۰% of the studies,pneumonia and urinary infections were discussed, and ICU was considered in terms of location.In all studies, artificial intelligence solutions had led to a constructive impact and reduced hospitalinfection rates. Machine learning algorithms (۴۸%) were the most significant artificial intelligenceapproach that was used in the field of prediction and control of hospital infections. Thenexpert systems played a major role in prevention and control. Among deep learning algorithms,convolutional neural network was the most used. In addition, research shows that the further developmentof artificial intelligence solutions in hospitals can provide unprecedented opportunitiesfor the control, diagnosis and treatment of nosocomial infection and similar futures.Conclusion: According to the findings of this study, it can be said that artificial intelligencesolutions can be effectively used to predict hospital infections and play a significant role in theprevention and diagnosis of hospital infections in a smart hospital.