Deep neural networks remain vulnerable to statistical variations in data despite advances in normalization techniques. Current approaches rely on fixed static normalization sets, fundamentally limiting their ability to adapt to dynamic data distributions. We introduce Dynamic Group Normalization (DGN), which treats channel grouping as a learnable component and leverages statistical awareness to form coherent groups adaptively. By employing an efficient spatio-temporal mechanism that continuously evaluates inter-channel relationships both within layers and across training epochs, DGN enables robust adaptation to evolving data distributions.Extensive evaluations across 24 architectures and 8 computer vision benchmarks demonstrate DGN's consistent superiority. Beyond achieving significant accuracy gains in classification, detection, and segmentation tasks while maintaining computational efficiency, DGN particularly excels in challenging scenarios where traditional methods struggle—notably in Out-Of-Distribution generalization and imbalanced data distributions.