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Understanding the relationship between atmospheric visibility and aerosol emission sources and identifying the key drivers of visibility have significant implications for the radiative forcing of aerosol. In this work, we combined the positive matrix factorization (PMF) model and machine learning (ML) models (the extreme gradient boosting model (XGBoost) and the Shapely additive explanations model (SHAP)) to identify the key drivers of visibility improvement based on long-term observations of visibility and PM2.5 composition in Shenzhen, China. From 2014 to 2021, the annual average levels of visibility increased from 17.2 to 27.0 km, which is tightly associated with the decreasing year by year PM2.5 concentrations. ML models, with distinct advantages in dealing with nonlinear relationships, revealed that secondary organic aerosol (SOA) is the major driver determining visibility, which is inconsistent with inorganic salts being the major driver identified by the widely used traditional linear method. Visibility improvement in Shenzhen was also found primarily driven by a decrease in SOA, highlighting that SOA in PM2.5 plays a critical role in radiative balance. This is the first study to investigate source impacts on atmospheric visibility using novel ML models, reflecting the great potential of ML methods in air pollution data analysis.