Preoperative evaluation is crucial for determining the appropriate surgical interventions and assessing patient risk. This review discusses current risk prediction tools and their limitations, emphasizing the shift from clinical estimation to statistical science. Risk prediction should consider a patient's baseline risk, personal circumstances, quality of life, and values. Various tools, including risk scores and prediction models, are used to estimate outcomes, but their accuracy is limited. Frailty assessment is increasingly important, with validated scoring systems helping predict morbidity and mortality. Cardiopulmonary exercise testing (CPET) is used to assess exercise capacity and guide surgical decisions. Biomarkers such as albumin, hemoglobin, and troponin are important in predicting outcomes. The ACS NSQIP Risk Prediction Model is a widely used tool for predicting postoperative complications. Future developments in big data and artificial intelligence may enhance risk prediction and improve patient outcomes. Overall, accurate risk assessment is essential for shared decision-making and personalized patient care.Preoperative evaluation is crucial for determining the appropriate surgical interventions and assessing patient risk. This review discusses current risk prediction tools and their limitations, emphasizing the shift from clinical estimation to statistical science. Risk prediction should consider a patient's baseline risk, personal circumstances, quality of life, and values. Various tools, including risk scores and prediction models, are used to estimate outcomes, but their accuracy is limited. Frailty assessment is increasingly important, with validated scoring systems helping predict morbidity and mortality. Cardiopulmonary exercise testing (CPET) is used to assess exercise capacity and guide surgical decisions. Biomarkers such as albumin, hemoglobin, and troponin are important in predicting outcomes. The ACS NSQIP Risk Prediction Model is a widely used tool for predicting postoperative complications. Future developments in big data and artificial intelligence may enhance risk prediction and improve patient outcomes. Overall, accurate risk assessment is essential for shared decision-making and personalized patient care.