Objective A predictive model for cognitive dysfunction in epileptic patients was constructed using LASSO regression, and the predictive efficacy of this model was analyzed. Methods A cross-sectional study was conducted, including epileptic patients from the NHANES database. Participants were categorized into cognitive dysfunction (DSST<34) and normal cognitive function groups (DSST≥34) based on the total score of the Digit Symbol Substitution Test (DSST). Data on demographics, socioeconomic characteristics, physical activity, medical history, as well as laboratory indicators such as 25-hydroxyvitamin D [25(OH)D] and neurofilament light chain (NfL) were collected. Lasso regression was used to select non-zero coefficient variables, and a multivariate logistic regression model was constructed to analyze factors affecting cognitive dysfunction in epileptic patients and to develop a nomogram prediction model. The model’s predictive ability was evaluated using receiver operating characteristic (ROC) curves, Bootstrap calibration curves, and Hosmer-Lemeshow goodness-of-fit tests. Results A total of 282 subjects were included, with a cognitive dysfunction incidence of 32.62% among the epileptic patients. Factors identified through Lasso reduction and logistic regression included blood uric acid (OR=2.098), NfL (OR=1.025), serum 25(OH)D (OR=0.989), and education level (OR=0.209~0.431). The nomogram prediction model based on these factors showed an area under the ROC curve of 0.833 (95% CI: 0.780–0.886), with a specificity of 0.784 and sensitivity of 0.761. The Bootstrap calibration curve demonstrated a high consistency between the observed probabilities and those predicted by the nomogram; the Hosmer-Lemeshow test also indicated good model fit (χ2=7.781, P=0.455). Conclusion Cognitive dysfunction in epileptic patients was influenced by factors including blood uric acid, NfL, serum 25(OH)D, and education level. The predictive model constructed based on Lasso regression exhibited good predictive efficacy. Identifying these factors provides important guidance for clinical assessment and intervention, contributing to improved management of prognosis in epileptic patients.