Objective  To develop a risk prediction model for mortality in maintenance hemodialysis (MHD) patients based on Logistic regression analysis and a nomogram. Methods Clinical data of 319 MHD patients admitted to Hezhou People's Hospital from January 2019 to January 2021 were retrospectively collected as the training cohort, and data of 80 MHD patients admitted from February 2021 to March 2022 were collected as the validation cohort. Two‑year follow‑up outcomes were reviewed, and patients were divided into a death group and a survival group based on mortality status. Lasso regression was used to screen covariates, and Logistic analysis was performed to identify factors influencing mortality. A nomogram prediction model for mortality in MHD patients was constructed using R software. Internal validation was conducted using the Bootstrap method. Receiver operating characteristic (ROC) curves and calibration curves were used to assess the model's discrimination and calibration, and decision curve analysis (DCA) was applied to evaluate the clinical utility of the nomogram. Results In the training cohort, there were 81 deaths and 238 survivors; in the validation cohort, 16 deaths and 64 survivors. In the training cohort, significant differences were observed between the death group and the survival group in age (t=5.497, P<0.001), diabetic kidney disease (χ²=28.016, P<0.001), vascular access type (χ²=34.491, P<0.001), high‑sensitivity C‑reactive protein (hs‑CRP) (t=42.746, P<0.001), hemoglobin (Hb) (t=15.038, P<0.001), albumin (ALB) (t=11.973, P<0.001), intact parathyroid hormone (iPTH) (t=2.937, P=0.004), and B‑type natriuretic peptide (BNP) (t=2.937, P=0.011). Six predictors were screened by Lasso regression and subsequently included in Logistic analysis, which identified age (OR=1.269, 95% CI: 1.146～1.405, P<0.001), diabetic kidney disease (OR=3.347, 95% CI: 1.149～9.749, P=0.001), vascular access type (OR=3.205, 95% CI: 1.212～8.476, P=0.016), hs‑CRP (OR=2.842, 95% CI: 1.922～4.204, P<0.001), Hb (OR=0.940, 95% CI: 0.910～0.971, P<0.001), and ALB (OR=0.814, 95% CI: 0.742～0.894, P<0.001) as independent factors influencing mortality in MHD patients. The ROC curve showed an AUC of 0.830 for the training model, with a C‑index of 0.830 by Bootstrap and a Hosmer–Lemeshow χ² of 7.621 (P=0.704). The DCA curve indicated a favorable net benefit within the probability range of 0.13–0.92. Internal cross‑validation in the validation cohort yielded an AUC of 0.844, a C‑index of 0.844, and a Hosmer–Lemeshow χ² of 7.412 (P=0.681), with the DCA curve showing a positive net benefit within the probability range of 0.12–0.89.  Conclusion Age, diabetic kidney disease, vascular access type, hs‑CRP, Hb, and ALB are factors influencing mortality in MHD patients. The nomogram prediction model based on Logistic regression demonstrates good discrimination and high net benefit, which may assist clinicians in optimizing treatment strategies.