Depression represents a major concern for cancer survivors, and early detection of depressive symptomatology is essential for timely intervention. This work set out to construct and validate a model capable of estimating depression risk in cancer survivors. A total of 2,279 cancer survivors from the National Health and Nutrition Examination Survey (NHANES) were analyzed. Participants were randomly divided into training and validation cohorts at a 7:3 ratio. Independent determinants of depression—defined as a PHQ-9 score ≥ 10—were identified using least absolute shrinkage and selection operator regression followed by multivariable logistic regression, and these factors were incorporated into a nomogram. Model discrimination was evaluated through receiver operating characteristic analysis, and its reliability was examined using calibration plots, the Hosmer–Lemeshow goodness-of-fit test, and decision curve analysis. Seven predictors were ultimately retained: age, education, poverty-to-income ratio, smoking behavior, congestive heart failure, sleep disorders, and number of cancers. A nomogram based on these variables was created. The area under the curve in the training cohort reached 0.802 (95% CI: 0.767–0.836), while the validation cohort yielded 0.794 (95% CI: 0.740–0.849). Bootstrapped internal validation generated an optimism-adjusted AUC of 0.812 (95% CI: 0.784–0.840). Calibration assessments confirmed strong agreement between predicted and observed risks, and decision curve analysis demonstrated meaningful clinical benefit. Collectively, this study presents a nomogram capable of estimating depression risk among cancer survivors, offering a potentially valuable tool for recognizing individuals who may require further psychological evaluation.
