Dynamic Neural Networks (DyNNs) are an emerging class of neural networks that adapt their structure or computation dynamically based on input or external conditions. Unlike static networks, which have a fixed architecture and parameters throughout inference, dynamic neural networks modify their behavior (such as layer depth, activation paths, or resource usage) in response to the input or available resources. This allows them to be more efficient, flexible, and scalable, especially in environments with variable computational power, real-time constraints, or heterogeneous data. Dynamic networks are particularly relevant in edge computing, real-time decision-making, and resource-constrained applications.Dynamic Neural Networks offer exciting opportunities to push the boundaries of neural network efficiency, scalability, and adaptability. Whether applied in edge computing, real-time systems, or healthcare, dynamic networks have the potential to revolutionize how we approach AI model deployment in variable and resource-constrained environments.