Background: Artificial intelligence (AI) is increasingly being explored in intensive and critical care for prediction, diagnosis, workflow optimization, and clinical training. With the rapid growth of machine learning applications in high-stakes environments such as intensive care units (ICUs) and emergency departments, evaluating their clinical utility and limitations is essential. Methods: A systematic search of PubMed, Scopus, Web of Science, and Embase was conducted for studies published between January 2020 and February 2025. Eligible studies included randomized trials, observational cohorts, and post hoc analyses that applied AI methods in critical care or emergency settings. Data on study design, patient population, AI methodology, and outcomes were extracted and synthesized narratively. Results: Ten studies were included, representing diverse settings such as ICUs, emergency departments, stroke centers, and oncology clinics. Populations ranged from critically ill patients with sepsis, hyperglycemic crises, trauma, and post–cardiac arrest to healthcare providers undergoing AI-assisted training. AI methods included random forest, multilayer perceptrons, artificial neural networks, extreme gradient boosting, and proprietary clinical decision support platforms. Findings demonstrated improvements in prediction accuracy (AUCs ranging from 0.79 to 0.97), workflow efficiency (e.g., 11.2-minute reduction in thrombectomy initiation), enhanced adherence to guidelines, and educational benefits. However, functional outcomes were inconsistently improved, and most studies highlighted challenges related to validation, methodological rigor, and real world applicability. Conclusion: AI applications show significant promise in enhancing predictive accuracy, clinical efficiency, and provider education in intensive and critical care. Despite these advances, widespread clinical adoption is hindered by concerns over external validation, methodological transparency, and integration into healthcare systems. Future research should prioritize rigorous validation and standardized reporting to ensure safe and effective translation into practice.