Perioperative risk assessment is a fundamental component of modern surgical care, aiming to identify patients at increased risk of postoperative complications, mortality, prolonged hospitalization, and healthcare resource utilization. Accurate risk stratification enables clinicians to optimize preoperative preparation, guide shared decision-making, allocate resources effectively, and improve surgical outcomes. Numerous perioperative risk assessment models have been developed, ranging from simple clinical scoring systems to sophisticated machine learning algorithms. Commonly used models include the American Society of Anesthesiologists (ASA) Physical Status Classification System, Revised Cardiac Risk Index (RCRI), Surgical Apgar Score, National Surgical Quality Improvement Program (NSQIP) Risk Calculator, and frailty assessment tools. This review examines major perioperative risk assessment models, their clinical applications, predictive performance, limitations, and emerging innovations involving artificial intelligence and precision medicine.