Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in social interaction, communication, and repetitive behaviors. The earlier the diagnosis, the more interventions and better developmental outcomes can be made. This research explores the capability of Generative Adversarial Networks (GANs) in enhancing machine learning models for the early detection of ASD by synthesizing data that enhances model robustness and accuracy. The dataset was trained on a combination of real and GAN-generated synthetic data to train machine learning models such as SVM, decision trees, and random forests. GANs were used to improve the diversity of the dataset and thereby enhance feature learning and generalization of the model. The model evaluation metrics included accuracy, precision, and recall. The integration of GAN-generated data significantly improved the model's performance. The best accuracy was found for the Random Forest model, which reached 93%, and its precision reached 90% along with a recall of 92%. Feature importance analysis gave the impression that dynamic functional connectivity, electroencephalography, and magnetoencephalography were the most critical in the prediction of ASD. The application of GANs is a promising approach to handling data limitations in the detection of ASD and enhances the performance and generalizability of machine learning models. More validation research is necessary on larger and more diverse datasets to validate the above findings.