Background: Suicide is common in patients with major depressive disorder (MDD) and has serious consequences for individuals and families. This study aims to establish a risk prediction model for suicide attempts in MDD patients to make the detection of suicide risk more accurate and effective. Methods: A cross-sectional survey, clinical examination, and biochemical indicator tests were performed on 1718 first-episode and drug nai ve patients with major depressive disorder. We used Machine Learning to establish a risk prediction model for suicide attempts in FEDN patients with MDD. Results: Five predictors were identified by LASSO regression analysis from a total of 20 variables studied, namely psychotic symptoms, anxiety symptoms, thyroid peroxidase antibodies (ATPO), total cholesterol (TC), and highdensity lipoprotein-cholesterol (HDL-C). The model constructed using the five predictors displayed moderate predictive ability, with an area under the ROC of 0.771 in the training set and 0.720 in the validation set. The DCA curve showed that the nomogram could be applied clinically if the risk threshold was between 22 % and 60 %. The risk threshold was found to be between 20 % and 60 % in external validation. Conclusion: Introducing psychotic symptoms, anxiety symptoms, ATPO, TC, and HDL-C to the risk nomogram increased its usefulness for predicting suicide risk in patients with MDD. It may be useful in clinical decisionmaking or in discussions with patients, especially in crisis interventions.