Dynamic Neural Networks (DNNs) represent an advanced paradigm in deep learning where network structures, parameters, or computational pathways adapt dynamically based on input data, task requirements, or resource constraints, enabling more flexible and efficient learning. Research in this field spans several directions, including dynamic routing mechanisms (e.g., Capsule Networks), adaptive depth and width adjustment for efficient inference, conditional computation where only relevant parts of the network are activated, and neural architecture search (NAS) for automated dynamic design. Studies also highlight the application of recurrent and continuous-time dynamic neural models for handling time-varying signals, control systems, and sequential decision-making, while spiking dynamic neural networks draw inspiration from biological processes for energy-efficient computation. Recent works emphasize resource-aware and edge-deployable dynamic neural networks that balance accuracy with latency and energy consumption, making them highly suitable for real-time applications in computer vision, natural language processing, autonomous driving, and Internet of Things (IoT) environments.