Objective To identify the risk factors of developing hyperuricemia （HUA）， especially due to exposure to chemical contaminants， common pesticides， and veterinary drugs in humans. Subsequently， the effect of machine learning techniques on exposure data of agricultural and veterinary drugs and chemical pollutants was explored.Methods According to the “Study on Appropriate Physical Activity to Reduce the Risk of Nutrition-related Chronic Diseases in Overweight Adults” program conducted in Shijiazhuang and Hangzhou， China， from 2018 to 2019， traditional logistic regression and random forest （RF） were used to establish prediction models using demographic indicators and exposure to pesticides， veterinary drugs， and chemical contaminantsas covariates on the development of HUA. The discrimination of the models were assessed by the area under the receiver operating characteristic curve （AUC）.Results RF analysis revealed that the top five factors affecting the development of HUA were doxycycline，4-chlorophenoxyacetate （4-CPA）， furaltadone， prochloraz， and perfluorodecanoic acid （PFDA）. The RF model showed better discriminant ability than the logistic regression model （AUC 0.934 vs. 0.735）.Conclusion Exposure to doxycycline， 4-CPA， furaltadone， prochloraz and PFDA， alcohol drinking history， living in Hangzhou， and a level of triglycerides ≥ 2.26 mmol/L may be risk factors for developing HUA. The RF model was suitable to analyze associations of chemical contaminants， pesticides， and veterinary drugs data， and ehibited a significantly improved discriminatory ability for identifying HUA patients compared with the conventional logistic regression model.