This review discusses the importance of perioperative risk assessment and outcome prediction, emphasizing the shift from simple risk scales to complex risk prediction models. The accuracy of risk estimation is enhanced by improved assessment of physical fitness, frailty, and the incorporation of biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. The article highlights the role of various tools, including categorical risk scales, risk scores, and risk prediction models, in estimating perioperative risk. It also explores factors predicting patient outcomes, such as age, functional status, comorbidities, and biomarkers like albumin, hemoglobin, and cardiac biomarkers. The importance of cardiopulmonary exercise testing (CPET) and frailty assessment in predicting postoperative complications is discussed. The article concludes by emphasizing the need for transparent and meaningful definitions of risks in patient-centered healthcare and the potential of big data and artificial intelligence to improve risk prediction and clinical practice.This review discusses the importance of perioperative risk assessment and outcome prediction, emphasizing the shift from simple risk scales to complex risk prediction models. The accuracy of risk estimation is enhanced by improved assessment of physical fitness, frailty, and the incorporation of biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. The article highlights the role of various tools, including categorical risk scales, risk scores, and risk prediction models, in estimating perioperative risk. It also explores factors predicting patient outcomes, such as age, functional status, comorbidities, and biomarkers like albumin, hemoglobin, and cardiac biomarkers. The importance of cardiopulmonary exercise testing (CPET) and frailty assessment in predicting postoperative complications is discussed. The article concludes by emphasizing the need for transparent and meaningful definitions of risks in patient-centered healthcare and the potential of big data and artificial intelligence to improve risk prediction and clinical practice.