Artificial Intelligence (AI) has the potential to revolutionize intraoperative neuromonitoring (IONM) by enabling real-time data analysis, enhancing signal interpretation, and offering predictive capabilities. AI processes vast amounts of neurophysiological data, helping clinicians identify subtle patterns that may be difficult for human observers to detect. This chapter explores the application of AI in IONM, providing an overview of machine learning, neural networks, and the evolving landscape of AI-driven neurophysiological monitoring during surgery. We also discuss the challenges and limitations, including data scarcity, variability in clinical protocols, and the ethical considerations that must be addressed as AI becomes more integrated into clinical practice.