Intensive Care Units (ICUs) serve a critical role in providing specialized care to critically ill patients in modern healthcare. The ICU environment poses numerous challenges, including high workload, complex decision-making, management of large healthcare data, and the consideration of individual patient needs and preferences. Decision support systems (DSS) in healthcare aim to improve care and aid decision-making by providing person-specific information and recommendations. Recent advancements in Artificial Intelligence (AI) have enabled the development of AI-based DSS, which analyze extensive medical data to identify patterns, make predictions, and personalize care. Various AI models and approaches, such as regression models, decision trees, random forest models, support vector machines, and neural networks have been employed to analyze patient data and help with decision making in the ICU. Evaluation of AI tool performance employs metrics such as accuracy, precision, sensitivity, and specificity, with validation against external databases being necessary. Gaining insights into decision-making factors, integrating subjective factors, and addressing ethical concerns are essential for the acceptance and deployment of AI-DSS in clinical practice. Clarification regarding responsibility, accountability, and the ability for clinicians to override AI-DSS recommendations is necessary to ensure appropriate utilization. While efforts have been made to evaluate AI-DSS in the ICU, most applications remain retrospective and lack clear evidence of improved clinician performance or patient outcomes. The majority of AI models rely on supervised learning for detection and identification tasks, but their efficacy is limited by the scarcity of high-quality data and inadequate consideration of human factors.. Furhtermore, the implementation of AI in the ICU still faces limitations and skepticism from healthcare personnel. Transparent explanations of modeling methods and operational procedures are necessary to instill clinician confidence. Addressing challenges such as the lack of standardized ICU databases and regulatory frameworks is pivotal for widespread adoption of AI in the ICU.