Background: Anesthesia technology generates continuous, high dimensional physiologic and drug infusion data, making it an ideal field for artificial intelligence (AI) applications. However, the extent to which AI tools improve clinical or process outcomes in anesthesia practice remains uncertain. We aimed to synthesize recent clinical studies evaluating AI applications embedded in anesthesia technology and to summarize evidence from broader systematic reviews and meta-analyses. Methods: A systematic review was conducted following PRISMA guidelines. Electronic databases and technical indexes were searched up to November 2025 for human studies in which AI models were used within anesthesia monitoring, hemodynamic management, or depth of anesthesia systems. We included original clinical studies reporting performance metrics or clinical outcomes, and recent systematic reviews and meta-analyses for contextual discussion. Results: Five original studies met inclusion criteria, these evaluated supervised or deep learning models for predicting post induction hypotension, forecasting intraoperative hypotension from waveform data, guiding intraoperative blood pressure management using a machine learning early warning system, and predicting anesthetic depth or infusion adjustments from drug histories and physiologic signals. AI models consistently showed better discrimination than conventional approaches. One randomized trial demonstrated a clinically meaningful reduction in intraoperative hypotension, though effects on major postoperative outcomes were inconclusive. Conclusion: AI enhanced anesthesia technology shows promising gains in predictive performance and intraoperative hemodynamic control, but evidence for downstream patient benefit remains limited. Larger, multi centre trials and robust external validation are needed before routine deployment.