Objective To construct a prediction model for the population with hypertension and diabetes to assess the risk of chronic kidney disease (CKD), and to provide a scientific basis for formulating targeted CKD prevention and control measures. Methods Based on physical examination data from community residents aged 60 years and above in Nanjing in 2022, 10 221 patients with hypertension and diabetes were selected as the study subjects. Variables associated with CKD prevalence were screened using univariate analysis, and further variable selection was performed using LASSO regression. Finally, a CKD risk prediction model was constructed based on logistic regression. The model's performance was evaluated using the ROC curve and calibration curve. Results The prevalence rate of CKD in the study population was 22.71%, with a mean age of 71.66 years. LASSO regression identified seven variables associated with CKD: age, blood urea nitrogen (BUN), hemoglobin, uric acid, triglyceride-glucose (TyG) index, urine protein-to-creatinine ratio (UPCR), and medical insurance type. The final logistic regression model incorporated six variables: age [OR=1.067 (95% CI: 1.058-1.076)], BUN [OR=1.377 (95% CI: 1.338-1.418)], hemoglobin [OR=0.992 (95% CI: 0.989-0.995)], uric acid [OR=1.004 (95% CI: 1.003-1.004)], TyG index [OR=1.445 (95% CI: 1.324-1.577)], and self-payment medical insurance [OR=1.732 (95% CI: 1.542-1.945)]. The model had an AUC of 0.759 (95% CI: 0.747-0.770) and a Brier score of 0.140 (95% CI: 0.136-0.145), indicating good predictive performance. The calibration curve showed good agreement between the predicted risk and the observed value. Conclusion The constructed LASSO-logistic regression risk prediction model in this study can effectively assess the risk of CKD in elderly individuals aged 60 years and above with hypertension and diabetes, providing a basis for early identification of high-risk individuals and the formulation of targeted CKD prevention and control measures.