Generative deep neural networks (DNNs) are an active research area in deep learning that focuses on models capable of generating new, realistic data samples by learning the underlying data distribution. Research papers in this domain explore architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Deep Boltzmann Machines (DBMs), and hybrid generative models, with applications in image synthesis, video generation, speech and audio synthesis, data augmentation, anomaly detection, and IoT data modeling. Key contributions include improvements in training stability, mode collapse prevention, conditional generation, multimodal data generation, and integration with reinforcement learning for controlled outputs. Recent studies also address challenges like high-dimensional data generation, evaluation metrics for generative quality, interpretability, and deployment in resource-constrained environments. By leveraging generative deep neural networks, research aims to enable realistic data generation, enhance downstream learning tasks, and support creative, predictive, and adaptive systems across diverse domains such as healthcare, finance, multimedia, and autonomous systems.