Deep neural networks (DNNs) are a central research area in artificial intelligence and machine learning, focusing on multi-layered neural architectures capable of learning complex representations from large-scale and high-dimensional data. Research papers in this domain explore applications across computer vision, natural language processing, speech recognition, healthcare, IoT, autonomous systems, and cybersecurity. Key contributions include convolutional neural networks (CNNs) for spatial data, recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) for sequential data, generative models such as autoencoders and GANs, and hybrid architectures integrating multiple DNN types. Recent studies also address challenges such as model interpretability, overfitting, adversarial robustness, computational efficiency, and deployment on resource-constrained devices using edge/fog computing. By leveraging deep learning and hierarchical feature extraction, research in deep neural networks aims to provide accurate, adaptive, and scalable solutions for complex real-world problems.