<p>Artificial intelligence (AI)-powered robotic surgery is reshaping the surgical landscape by enhancing decision-making, precision, and patient safety in complex procedures. Despite its remarkable progress, current literature reveals critical gaps in interoperability, ethical governance, surgeon training, and real-time data integration between AI systems and human judgment. This study aims to examine how AI-powered robotic systems transform intraoperative decision-making and optimize surgical outcomes across minimally invasive, oncological, and cardiac operations. The review synthesizes quantitative insights from performance metrics and qualitative evaluations from clinical experiences, integrating multidisciplinary studies on intelligent robotics, augmented reality (AR), and machine learning in surgery. Comparative analysis highlights significant advancements in automation, preoperative planning, and intraoperative visualization, while revealing contradictions between human-centric adaptability and machine autonomy. Findings underscore that AI integration enhances surgical precision, reduces complication rates, and supports predictive analytics, yet faces limitations in ethical regulation, cost-effectiveness, and surgeon acceptance. Recommendations emphasize transparent algorithmic design, cross-specialty training, and collaborative frameworks between engineers and clinicians to ensure safe adoption. The implications extend to redefining surgical education, standardizing robotic protocols, and fostering patient-centered AI governance. Limitations include the narrow empirical scope and evolving regulatory landscape. Future research should explore adaptive AI learning models, global benchmarking, and equitable access to robotic technologies. Ultimately, AI-powered robotic surgery symbolizes a fundamental transformation toward data-driven, personalized, and ethically guided healthcare transformation.</p>