Deep learning for intelligent wireless networks is a rapidly evolving research area focused on applying advanced neural network architectures to enhance the performance, reliability, and efficiency of wireless communication systems. Research papers in this domain explore applications such as resource allocation, spectrum sensing, interference management, beamforming, network traffic prediction, anomaly detection, and energy-efficient communication in 5G, 6G, IoT, and vehicular networks. Key deep learning models include convolutional neural networks (CNNs) for spatial pattern recognition, recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) for temporal traffic prediction, autoencoders for feature extraction, and reinforcement learning-based networks for adaptive control. Recent studies address challenges such as high-dimensional data, dynamic network environments, low-latency requirements, distributed and edge deployment, and integration with software-defined networking (SDN) and network function virtualization (NFV). By leveraging deep learning, research in intelligent wireless networks aims to provide adaptive, efficient, and scalable solutions that enhance network throughput, reliability, security, and quality of service in complex wireless environments