Objective To explore the pathways linking residential environmental factors with cognitive impairment among empty-nest older adults, aiming to identify key direct and indirect predictors and provide a basis for targeted environmental interventions. Methods Based on the theoretical framework of the socio-ecological model, data were obtained from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), including a total of 5,961 empty-nest older adults. Potential predictors were initially screened through univariate analysis and LASSO regression. The Bayesian network structure was constructed using the R bnlearn package, with the Tabu search algorithm applied for structure learning and Bayesian estimation for parameter learning. Probability predictions were performed using Netica software.Results The incidence of cognitive impairment among empty-nest older adults was 18.7%. After LASSO regression screening, 11 variables were included in the Bayesian network model. The Bayesian network model identified four factors directly associated with cognitive impairment:smoking, accessibility of healthcare services, presence of mold smell in the room, and frequency of pesticide use. Seven factors were indirectly linked to cognitive impairment: frequency of window ventilation in summer, use of pipeline natural gas, kitchen ventilation, residential location, frequency of mosquito repellent use, frequency of window ventilation in winter, and availability of community health education. The subgroup with all four risk factors:smoking, presence of mold smell in the room, lack of accessible healthcare services, and frequent pesticide use had the highest risk of cognitive impairment, reaching 71.0%. Conclusion Residential environmental factors are associated with cognitive function in empty-nest older adults through multiple pathways. Prevention strategies for cognitive impairment should adopt a multi-factorial and integrated intervention model involving both community and household approaches.