Generative Adversarial Networks (GANs) are a powerful class of deep learning models that learn to generate realistic data by pitting two neural networks—a generator and a discriminator—against each other in an adversarial setting. Introduced by Goodfellow et al., GANs have since become a cornerstone for generative modeling, enabling high-fidelity image synthesis, video generation, data augmentation, style transfer, and domain adaptation. Early research focused on stabilizing training and improving convergence, while subsequent studies explored variants such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), Wasserstein GANs (WGANs), and CycleGANs for unpaired image-to-image translation. GANs have been applied in computer vision, medical imaging, speech synthesis, natural language generation, and anomaly detection. Recent advancements include integration with attention mechanisms, progressive training, multimodal generation, and incorporation into semi-supervised and reinforcement learning frameworks. Research also addresses challenges like mode collapse, training instability, evaluation metrics, and ethical concerns in synthetic data generation. GANs continue to be a foundational technique for learning complex data distributions and enabling realistic generative AI applications.