Background: Clinical indicators for accurately distinguishing diabetic nephropathy (DN) from non-diabetic renal disease in type 2 diabetes (T2D) are lacking. This study aimed to develop and validate a nomogram for predicting DN in T2D patients with kidney disease. Methods: A total of 302 consecutive patients with T2D who underwent renal biopsy at China-Japan Friendship Hospital between January 2014 and June 2019 were included in the study. The data were randomly split into a training set containing 70% of the patients (n = 214) and a validation set containing the remaining 30% of patients (n = 88). Multivariable logistic regression analyses were applied to develop a prediction nomogram incorporating the candidates selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using a concordance index (C-index), calibration plot, and decision curve analysis. Both internal and external validations were assessed. Results: A multivariable model that included gender, diabetes duration, diabetic retinopathy, hematuria, glycated hemoglobin A1c, anemia, blood pressure, urinary protein excretion, and estimated glomerular filtration rate was represented as the nomogram. The model demonstrated very good discrimination with a C-index of 0.934 (95% CI 0.904-0.964). The calibration plot diagram of predicted probabilities against observed DN rates indicated excellent concordance. The C-index value was 0.91 for internal validation and 0.875 for external validation. Decision curve analysis demonstrated that the novel nomogram was clinically useful. Conclusion: The novel model was very useful for predicting DN in patients with T2D and kidney disease, and thereby could be used by clinicians either in triage or as a replacement for biopsy.