Background <p>Adolescent depression is a rising public health issue, particularly in China, due to increased social and changing family dynamics. This study aimed to develop a nomogram model that integrates individual traits, family background, and social support factors to improve the early detection and intervention of adolescent depression.</p> Methods <p>This study involved 943 adolescents (232 cases and 711 controls). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select key predictive variables, and a multivariate logistic regression model was constructed. The model’s performance was evaluated through receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) to assess its discriminatory power, calibration, and clinical utility.</p> Results <p>The nomogram demonstrated high discriminatory capacity with areas under the curve of 0.988 for the training set and 0.987 for the validation set. Independent risk factors included self-harm behavior, poor familial relationships, and reduced sleep quality. The calibration curves showed strong concordance between predicted probabilities and actual outcomes, as validated by the Hosmer-Lemeshow test (<i>P</i> = 0.671). DCA revealed that the nomogram offered superior net benefits over traditional screening methods, particularly in identifying high-risk individuals when the risk threshold exceeded 20%.</p> Discussion <p>This study developed a nomogram incorporating key risk factors, with sleep quality and self-harm behavior emerging as strong predictors of depression. Strained family dynamics also contribute to depression risk. The model’s high accuracy makes it a valuable tool for early clinical intervention.</p>