Objective:To develop a prediction model using machine learning to identify anxiety and depression in elderly individuals.Methods:This study collected data from 15079 elderly individuals in Shanxi Province, including their social demographic factors and disease status.Anxiety and depression were evaluated using GAD-7 and PHQ-9 scales to understand the characteristics of mental illness in the elderly.The evaluation indexes included accuracy, recall, precision, F1 score, Receiver Operating Characteristic Curve(ROC), and area under the curve(AUC), which were derived from the confusion matrix and several models.Results:The output of our study clearly demonstrates that the full feature prediction based on LightGBM is highly accurate, with an AUC of 0.805[95% CI: 0.794-0.811]. This outperforms the Random Forest model, which achieved an AUC of 0.730[95% CI: 0.702-0.741], and the XGboost model, which achieved an AUC of 0.802[95% CI: 0.780-0.807]. Therefore, LightGBM algorithm proves to be a strong prediction model.Our simplified model, based on eight selected features, also achieves a respectable AUC of approximately 0.75. Conclusions:The new prediction model for anxiety and depression specifically designed for the elderly can be effectively utilized in grassroots health surveys or for self-examinations to efficiently predict anxiety and depression levels among the elderly population in the community.