Objective To construct a hypoglycemia risk prediction model for hospitalized elderly patients with type 2 diabetes, and to provide healthcare professionals with a reference for early identification of high-risk individuals and targeted interventions. Methods A hypoglycemia risk factor screening checklist was developed through literature review, two rounds of expert consultation, and discussion within the research team. A total of 585 elderly patients with type 2 diabetes, admitted to the endocrinology department from June to October 2023, were included as study subjects, and data were collected accordingly. Lasso regression and multivariate logistic regression analyses were used to screen for hypoglycemia risk factors, and a nomogram prediction model was established. The model′s performance was internally validated using both the full sample set and a 500-iteration Bootstrap approach. Results Among the 585 patients, 193 (32.99%) experienced hypoglycemia. Lasso regression identified 24 variables, of which 11 (gender, diastolic blood pressure, monthly per capita household income, alcohol consumption, frequency of hypoglycemia in the past year, awareness of hypoglycemia symptoms, hypertension, hyperlipidemia, polypharmacy, random C-peptide level, and glycated hemoglobin) were used to construct the nomogram. The area under the ROC curve (AUC) was 0.830 in the full sample data set and 0.821 in the Bootstrap validation, with Brier scores of 0.156 and 0.159, sensitivity of 0.702 and 0.747, and specificity of 0.839 and 0.809, respectively. Conclusion The hypoglycemia risk prediction model for hospitalized elderly patients with type 2 diabetes demonstrates good discrimination and calibration, providing a reference for clinical healthcare professionals in identifying high-risk patients for hypoglycemia.