Autistic Spectrum Disorder (ASD) refers to a group of developmental disorders that affect the nervous system, leading to challenges in social interaction, communication, and behavior. The severity of ASD symptoms can vary widely, ranging from mild to severe. Diagnosing and predicting ASD with high accuracy requires the use of advanced machine learning models. However, one of the major challenges in building such models is the availability of sufficient data. Open-source datasets often have a limited number of instances, which may not be enough to train robust models that can generalize well to new, unseen data. To overcome this limitation, it is essential to augment the dataset with additional, synthetically generated instances. In this context, techniques like corGAN (Conditional Generative Adversarial Networks) are employed. This comprehensive dataset is then used to train a machine learning model, which can more effectively predict ASD. The synthetic data ensures that the model has access to a richer, more varied set of information, ultimately leading to better performance and more accurate predictions for diagnosing and understanding ASD. We will also apply SMOTHE and Adaptive_Synthetic on GAN data, and prove that SMOTHE on GAN data gave a better distribution than Adaptive_Synthetic on GAN data.