Objective To identify key influencing factors for comorbidity between myopia and depressive symptoms among middle school students, and to construct a nomogram predictive model capable of quantitatively assessing their comorbidity risk. Methods A total of 13 976 middle school students aged 11-20 from 14 counties (cities, districts) in Yichang City were selected as study subjects through cluster random sampling. On-site surveys were conducted using the Student Health Status and Influencing Factors Questionnaire and the Depression Assessment Scale. Chi-square test and multivariate logistic regression analysis were employed to investigate the comorbidity association between myopia and depressive symptoms and their influencing factors, and to construct a nomogram model for risk prediction. Results The myopia screening rate among middle school students was 68.67%, the depression detection rate was 23.31%, and the comorbidity detection rate was 16.59%. Logistic regression analysis revealed that boarding school (OR=1.207), smoking history (OR=1.504), alcohol consumption (OR=2.016), outdoor activity time <1 h (OR=1.293), internet addiction (OR=4.008), school bullying (OR=4.358), school absence due to illness within one year (OR=1.712), and severe injury within one year (OR=1.356) were risk factors for myopia-depression comorbidity. Protective factors included male gender (OR=0.578), junior high school students (OR=0.670), nuclear family (OR=0.862), moderate-to-high intensity exercise ≥1 h/d (OR=0.852), screen time <2h (OR=0.803), adequate sleep duration (OR=0.602), and homework time after school <2 h/d (OR=0.766). The nomogram model constructed based on the above variables exhibited excellent discriminant power (AUC = 0.712) and calibration (Hosmer-Lemeshow test, χ²=3.245, P> 0.05). Conclusion Factors such as boarding school attendance, smoking history, alcohol consumption, less than 1 hour of daily outdoor activity, internet addiction, and school bullying are significantly associated with the comorbidity of screening myopia and depressive symptoms among middle school students in Yichang City. The nomogram model effectively predicts the comorbidity risk in this population, facilitating timely intervention for high-risk individuals.