This study aims to identify the risk factors associated with distal symmetric polyneuropathy (DSPN) in patients with type 2 diabetes mellitus (T2DM) and to develop a predictive model for assessing the risk of DSPN. Methods: A total of 1,658 eligible T2DM patients were selected from Xiaogan Hospital, affiliated with Wuhan University of Science and Technology, between July 2021 and July 2024. General and clinical data were collected and then split into a training set and a validation set in a 7 ∶ 3 ratio. LASSO regression and binary logistic regression were employed to develop a nomogram model using the training set data, which was subsequently validated with the validation set data. The model’s accuracy, discrimination, and clinical applicability were assessed using calibration curves, the area under the receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results: The analysis identified duration of diabetes (OR=1.195, 95% CI: 1.116-1.280), fasting blood glucose (FPG) (OR=1.614, 95% CI: 1.435-1.816), neutrophil-to-lymphocyte ratio (NLR) (OR=1.388, 95%CI: 1.042-1.849) and urinary microalbumin (mALB) (OR=1.536, 95% CI: 1.113-2.120) as independent risk factors for DSPN. Conversely, high-density lipoprotein cholesterol (HDL-C) (OR=0.252, 95%CI: 0.160-0.397) and 25-hydroxyvitamin D[25(OH)D] (OR= 0.845, 95% CI: 0.825-0.864) were identified as independent protective factors. The nomogram model’s predicted DSPN risk probability closely aligned with the actual probability, as demonstrated by the calibration curve. The area under the curve (AUC) for DSPN prediction was 0.896 (95% CI: 0.878-0.913) in the training group and 0.888 (95%CI: 0.860-0.917) in the validation group. The decision curve analysis (DCA) indicated that the model holds significant clinical value across a wide range of thresholds. Conclusion: This study successfully developed a highly accurate nomogram prediction model based on key predictors, including diabetes duration, FPG, NLR, mALB, HDL-C, and 25(OH)D.