A Survey of Deep Learning Solutions for Network Routing in Modern Networks: Challenges and Opportunities

The rise of data-driven networks and emerging technologies like the Internet of Things (IoT) and Mobile Ad Hoc Networks (MANETs) has intensified research on optimizing routing mechanisms. Traditional protocols often struggle with the scalability and adaptability demands of modern networks. Deep Learning (DL) techniques, including Deep Reinforcement Learning (DRL), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), offer promising solutions by enabling real-time adaptive routing. This survey reviews state-of-the-art DL-based routing approaches, highlighting their strengths and research directions. Findings suggest that models like RNNs, Graph Neural Networks (GNNs), and DRL can significantly enhance routing efficiency in dynamic, large-scale networks.