Background: Sepsis is a leading cause of morbidity and mortality, with existing diagnostic tools and biomarkers often proving insufficient for timely recognition. Recent advances in artificial intelligence (AI) and machine learning (ML) have shown potential to improve the early detection and prediction of sepsis using routinely available clinical and laboratory data. Methods: This systematic review was conducted according to PRISMA guidelines. A comprehensive search of PubMed, Scopus, Web of Science, and IEEE Xplore was performed for studies published between January 2017 and July 2025. Eligible studies applied AI or ML methods to predict sepsis or bacteremia in human populations using laboratory or electronic health record data and reported model performance metrics such as area under the receiver operating characteristic curve (AUC), sensitivity, or specificity. Data extraction and quality assessment using the Prediction Model Risk of Bias Assessment Tool (PROBAST) were conducted independently by two reviewers. Given the heterogeneity of study designs and outcomes, a narrative synthesis was performed. Results: Ten studies met the inclusion criteria, with diverse populations from neonates to critically ill adults and sample sizes ranging from 32 to over 366,000. Most models incorporated complete blood count (CBC), inflammatory biomarkers, or electronic health records, with methods including support vector machines, random forest, gradient boosting, and neural networks. Reported AUCs ranged from 0.79 to 0.99, with ML models generally outperforming conventional clinical scores such as SOFA and SIRS. Adult-focused studies consistently demonstrated strong predictive performance, while results in neonatal and pediatric populations were less robust. Despite promising results, several studies highlighted concerns regarding heterogeneity, limited external validation, and challenges with clinical integration. Conclusions: AI and ML models hold significant promise for improving the early detection and prediction of sepsis using routinely available data. These tools consistently outperform conventional diagnostic methods in adult populations, though evidence in neonates and children remains limited. Future research should prioritize multicenter prospective validation, standardization of predictor sets, and evaluation of real-world clinical impact to enable safe and effective implementation of AI-based decision support in sepsis care.