Objective: To analyze the clinical characteristics and related influencing factors in drug‑naive patients with newly‑diagnosed Parkinson's disease accompanied by depression (Depression in Parkinson's disease, DPD), and to construct a nomogram risk‑prediction model. Methods: A total of 268 consecutive drug‑naive patients with newly‑diagnosed Parkinson's disease (PD) admitted to the Department of Neurology, Jiangning Hospital Affiliated to Nanjing Medical University from October 2021 to July 2025 were enrolled. General clinical data and results of relevant scale assessments were collected. Patients were divided into two groups according to the 24‑item Hamilton Depression Rating Scale (HAMD‑24): patients with HAMD‑24 score <8 points were assigned to the non‑DPD group, and those with HAMD‑24 score ≥8 points to the DPD group. Univariate analysis was performed to compare inter‑group differences in general clinical data and clinical scale scores. Variables with statistically significant differences screened by univariate analysis were included in the multivariate Logistic regression model to identify independent risk factors for depression in drug‑naive newly‑diagnosed PD patients. A nomogram was plotted based on these factors to establish the DPD risk‑prediction model. Results: Univariate analysis revealed statistically significant inter‑group differences in UPDRS-Ⅱ, UPDRS-Ⅲ, Hoehn‑Yahr (H‑Y) stage, HAMA, MMSE, MoCA, PDSS, NMSQ, RBD, olfactory score and KPPS score (all P<0.05). These differentially distributed variables were further entered into the multivariate Logistic regression model. The results demonstrated that HAMA, PDSS, RBD, olfactory score and KPPS score were independent influencing factors for depression in newly‑diagnosed PD patients (all P<0.05). The prediction model built on the five independent risk factors yielded an area under the receiver‑operating characteristic (ROC) curve of 0.796 (95%CI 0.755～0.837), indicating favorable discriminatory power of the model. The calibration curve showed high consistency between model‑predicted values and actual observed values. Decision‑curve analysis suggested prominent clinical net benefit and satisfactory applicability of the model. Conclusion: HAMA, PDSS, RBD, olfactory score and KPPS score are independent influencing factors for depression in drug‑naive newly‑diagnosed PD patients. The prediction model constructed from the above indicators exhibits good predictive performance.