Objective: To conduct an in-depth analysis of the key factors influencing the risk of malnutrition in elderly diabetic patients with cognitive frailty (CF) and to construct an accurate risk prediction nomogram model based on these factors. Methods: A cross-sectional study design was adopted, enrolling 124 elderly diabetic patients with CF admitted to the Department of Geriatric Endocrinology, the First Affiliated Hospital of Nanjing Medical University between January 2023 and December 2024. The study instruments included: a general information questionnaire, the Fried Phenotype Frailty scale, the Montreal Cognitive Assessment scale, the Geriatric Depression Scale (GDS), the Self-Rating Anxiety Scale, and the Mini Nutritional Assessment scale. Basic patient information was collected, and assessments were made for frailty, cognitive function, psychological status, and malnutrition risk. Multivariate logistic regression analysis was used to identify influencing factors for malnutrition risk, and the R language "rms" package was used to construct the prediction model and draw the nomogram. Model validation included Receiver Operating Characteristic (ROC) curve analysis, the Hosmer-Lemeshow test, assessment of the Concordance Index (C-index), and the plotting of a calibration curve. Results: The malnutrition risk rate among the 124 elderly diabetic patients with CF was 67.7% (84/124). These 84 patients were assigned to the malnutrition risk group, and the remaining 40 were assigned to the well-nourished group. Significant differences (P < 0.05) were found between the two groups in terms of age, marital status, body mass index (BMI), GDS score, albumin, prealbumin, transferrin, and hemoglobin levels. Age, marital status, BMI, GDS score, albumin, and prealbumin levels were identified as independent predictors of malnutrition risk in elderly diabetic patients with CF (P < 0.05). The nomogram model constructed based on these influencing factors had a C-index of 0.781 [95% CI (0.695-0.867)]. The Hosmer-Lemeshow test indicated a good fit for the nomogram model (χ2 statistic = 5.22, corresponding P-value = 0.73). Decision curve analysis showed that when the threshold probability ranged from 0 to 0.67, the net benefit rate of using this nomogram model to predict malnutrition risk in elderly diabetic patients with CF was > 0. Conclusion: In elderly diabetic patients with CF, age, marital status, BMI, GDS score, albumin, and prealbumin levels are key factors influencing the risk of malnutrition. The established risk prediction model demonstrates moderate predictive performance and good clinical application value for assessing the risk of malnutrition in this patient population.