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This study involves data augmentation modeling using Generative Adversarial Networks (GAN) on the tensile test data of austenitic stainless steel, which encompasses chemical compositions, heat treatments, and mechanical properties. The synthetic data generated by GAN is subsequently used as the training dataset for six different algorithm models. The best-performing algorithm is selected based on the best evaluation metric values. The results of the Kolmogorov–Smirnov (KS) test indicate that the distribution of synthetic data does not significantly differ from the distribution of experimental data. Furthermore, the training results of predictive models employing synthetic data with the six machine learning algorithms demonstrate that the gradient boosting model exhibits superior performance in predicting the mechanical properties of austenitic stainless steel.