Early identification of frailty is challenging in clinical settings due to its insidious onset and progression. Assessing real-life changes in behavioral and physical health may facilitate early frailty identification from home settings. The goal of this paper is to determine the performance of machine learning models for identifying frailty using behavioral and physical health features. This study re-used the dataset from the Survey of Health, Ageing and Retirement in Europe, and implemented machine learning classifiers to classify frailty. We selected twenty-two features from the dataset. The classification performance was evaluated using the area under the receiver operating characteristic curve (AUC ROC) and precision-recall curve. As a result, the Gradient Boosting classifier achieved the highest cross-validated AUC ROC (0.9453) and precision-recall curve (0.7029). Mobility limitations and physical inactivity were the top two most important features among the 22 features. In summary, machine learning methods can accurately identify frailty using the selected behavioral and physical health features. The findings have significant implications for identifying frailty much earlier using data in individuals’ real-life before clinical frailty assessment.