Explores the utilization of Generative Adversarial Networks (GANs) for generating synthetic images to address challenges related to limited data availability and privacy concerns. GANs, trained on diverse datasets, are capable of producing high-fidelity images from random noise. For example, training a GAN on the MNIST dataset allows for the creation of digit images that closely resemble those in MNIST, which can then be used to train other neural networks. Our evaluations demonstrate that these synthetic images maintain high fidelity and are effective in various tasks, including classification and object detection. This highlights the potential of GANs for dataset augmentation and enhancement of model performance, providing a viable solution for expanding training datasets while preserving data privacy.