Soil heterogeneity makes the vertical distribution of metal(loid)s in site soil vary considerably and poses a challenge for identifying the key factors of metal(loid)s migration in site soil profiles. In this study, a machine learning (ML) model was developed to study a typical abandoned Pb/Zn smelter using 267 site soils from 46 drilling points. Results showed that a well-trained ML model could be used to identify the key factors in determining the contamination vertical distribution and predict the metal(loid)s contents in subsurface soil. As, Cd, Pb, and Zn were the primary pollutants and their vertical migration depth arrived to 4–6 m. Based on the predictive performance of different ML algorithms, the extreme gradient boosting (XGB) was selected as the best model to produce accurate predictions for the most metal(loid)s content. Contents of As, Cd, Pb, and Zn in the heavily contaminated zones declined with an increase of soil depth. The metal(loid) contents in surface soil of 0–2 m could be readily used to predict the content of Cd, Cr, Hg, and Zn in subsurface soil from 2 m to 10 m. Based on the metal-specific XGB models, sulfur content, functional area, and soil texture were identified as key factors affecting the vertical distribution of As, Cd, Pb, and Zn in site soil. Results suggested the ML method is helpful to manage the potential environmental risks of metal(loid)s in Pb/Zn smelting site. Display omitted •As, Cd, Pb, and Zn were the primary contamination in Pb/Zn smelting site soil.•Machine learning algorithms were developed to predict metal(loid)s contamination.•Extreme gradient boosting (XGB) model outperformed in interpreting non-linear soil data.•Key factors of metal(loid)s vertical distribution in site soil were identified.