Purpose: To explore the influencing factors of acute kidney injury in elderly patients with diabetic nephropathy and to construct a nomogram model. Methods: The research subjects were 680 patients with type 2 diabetic nephropathy admitted to our hospital. The patients were included from May 2018 to August 2023. Patients with acute kidney injury were used as the merge group (n=50), and patients without unmerge group (n=630) was included. The prevalence and predisposing factors of acute kidney injury in diabetic nephropathy were analyzed, multivariate logistic regression were used to analyze the influencing factors of acute kidney injury in patients, and a nomogram risk prediction model was established based on risk factors for verification. Results: Analysis of the factors of acute kidney injury in diabetic nephropathy found that severe infection was the main trigger, accounting for 40.00%, followed by nephrotoxic antibiotics and severe heart failure. The age, urine microalbumin-to-creatinine ratio (ACR), blood urea nitrogen (BUN), uric acid(UA), and cystatin C (CysC) levels of patients in the combined acute kidney injury group were significantly higher than those in the unmerge group (P<0.05), and the left ventricular ejection fraction (LVEF) and epidermal growth factor receptor (eGFR) levels were significantly lower than those in the unmerge group (P<0.05). Age, ACR, and CysC levels are independent risk factors for acute kidney injury in diabetic nephropathy, and LVEF and eGFR are independent protective factors (P<0.05). The C-index of the nomogram risk prediction model in predicting acute kidney injury in diabetic nephropathy is 0.768 (95% CI: 0.663-0.806), and the calibration curve tends to the ideal curve; the prediction threshold is >0.18, and the nomogram risk prediction model provides a clinical net benefits, and clinical net benefits were higher than independent predictors. Conclusion: The establishment of a nomogram model for acute kidney injury in elderly patients with diabetic nephropathy based on age, ACR, CysC, LVEF, and eGFR has a good predictive effect, which can help doctors more accurately assess the patient's condition and provide a basis for formulating personalized treatment plans.