Antioxidants are widely studied compounds with significant applications in the nutraceutical and dietary industries. To enable the rapid screening of large libraries of substances for antioxidant activity and to provide a useful tool for the initial evaluation of substances of interest with unknown activity, we developed Quantitative Structure–Activity Relationship (QSAR) models to predict the antioxidant potential of chemical substances. We started from a dataset of 1911 antioxidant substances, retrieved from the AODB database by selecting the DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging activity assay and the experimental value of the half-maximal inhibitory concentration. Different machine learning algorithms were applied to build regression models, and the goodness-of-fit of each model was assessed using the statistical parameters of R squared (R²), the Root-Mean-Squared Error, and the Mean Absolute Error. The Extra Trees model outperformed the other models in both internal and external validations, achieving the highest R² of 0.77 and the lowest RMSE on the test set. Gradient Boosting and eXtreme Gradient Boosting also achieved promising results with R² values of 0.76 and 0.75, respectively. Given these results, we developed an integrated method that not only outperformed the individual models, achieving an R² of 0.78 on the external test set, but also provided valuable insights into the range of predicted values.