Introduction In the operating room, a matter of seconds can make a significant difference in patient safety. While the anesthesia team monitors vital signs such as heart rate, blood pressure, and respiratory rate, emerging digital technologies are capable of continuously analyzing data streams and detecting early indicators that may require medical attention. In this context, artificial intelligence (AI) emerges as a supportive tool for the medical team, contributing to enhanced monitoring accuracy and improved decision-making without replacing human expertise or clinical judgment. Applications of Artificial Intelligence in Anesthesia First: Predicting Intraoperative Hypotension Intraoperative hypotension is one of the major challenges in anesthesia practice, as it may affect the perfusion of vital organs. One of the most prominent applications of AI in this field is the use of systems that analyze arterial blood pressure waveforms. These systems can examine changes in waveform patterns and provide early alerts regarding the potential occurrence of hypotension. Such alerts help anesthesiologists assess the patient’s condition and prepare for appropriate interventions before the situation worsens. However, these predictions do not constitute a confirmed diagnosis and may be associated with inaccurate alerts. Therefore, interpreting the results in the context of the patient’s clinical condition remains essential. Second: Enhancing Anesthesia Depth Monitoring Monitoring the depth of anesthesia is a fundamental aspect of ensuring patient stability during surgical procedures. This monitoring relies on several indicators, including electrical signals from the brain, alongside other vital signs. Recent studies are exploring the use of AI algorithms to analyze these signals, with the aim of providing more accurate information regarding the brain’s response to anesthetic drugs and assisting in estimating the level of anesthesia. Nevertheless, it is important to distinguish between conventional digital monitoring devices and systems that genuinely incorporate AI technologies. Third: Risk and Complication Assessment Before and After Surgery AI-powered computational models can analyze a patient’s medical history, laboratory results, and health-related risk factors to estimate the likelihood of certain complications occurring during or after surgery. These technologies can also be used during the postoperative period to identify patients who may require closer monitoring and continuous follow-up. The effectiveness of these models depends on the quality of the data used for training and the extent to which their accuracy has been validated across diverse clinical settings. Artificial Intelligence in Anesthesia Techniques Education and Training The role of AI is not limited to clinical applications; it also extends to educational and training fields. Intelligent simulation can be utilized to train anesthesia techniques students in managing emergency situations, such as decreased blood oxygen levels, sudden changes in blood pressure, or cardiac rhythm disturbances. These virtual environments provide students with opportunities to analyze vital indicators, prioritize actions, and make appropriate decisions within scenarios that simulate real clinical situations, while ensuring a safe educational environment under the supervision of academic staff. This contributes to developing practical skills and enhancing preparedness for managing critical cases. Challenges and Ethical Considerations Despite the promising potential of AI in anesthesia, its implementation faces several challenges, most notably protecting patient data privacy, minimizing algorithmic bias, ensuring the accuracy of results, and providing healthcare professionals with continuous training to use these systems effectively.Furthermore, it is necessary to distinguish between automated systems that rely on conventional control algorithms and those that genuinely utilize AI technologies. The mere automation of drug administration or vital sign monitoring does not necessarily indicate the use of artificial intelligence. Accordingly, the continuous scientific evaluation of these technologies and the verification of their effectiveness and safety are essential before their widespread adoption in clinical practice. Conclusion The future of anesthesia does not lie in operating rooms functioning without human involvement, but rather in an integrated medical environment that combines the precision of intelligent devices with the expertise of the medical team and its ability to make appropriate clinical decisions. Artificial intelligence represents an important opportunity to advance anesthesia practice through enhanced continuous monitoring, risk prediction, and improved medical education and training. The more these technologies are grounded in scientific evidence, rigorous evaluation, and responsible implementation, the greater their potential to support patient safety and elevate the quality of healthcare. Al Mustaqbal University The First University in Iraq