Recently, MLP-based architectures achieved impressive results in image classification against CNNs and ViTs. However, there is an obvious limitation in that their parameters are related to image sizes, allowing them to process only fixed image sizes. Therefore, they cannot directly adapt dense prediction tasks (e.g., object detection and semantic segmentation) where images are of various sizes. Recent methods tried to address it but brought two new problems, long-range dependencies or important visual cues are ignored. This paper presents a new MLP-based architecture, Region-aware MLP (RaMLP), to satisfy various vision tasks and address the above three problems. In particular, we propose a well-designed module, Region-aware Mixing (RaM). RaM captures important local information and further aggregates these important visual clues. Based on RaM, RaMLP achieves a global receptive field even in one block. It is worth noting that, unlike most existing MLP-based architectures that adopt the same spatial weights to all samples, RaM is region-aware and adaptively determines weights to extract region-level features better. Impressively, our RaMLP outperforms state-of-the-art ViTs, CNNs, and MLPs on both ImageNet-1K image classification and downstream dense prediction tasks, including MS-COCO object detection, MS-COCO instance segmentation, and ADE20K semantic segmentation. In particular, RaMLP outperforms MLPs by a large margin (around 1.5% Apb or 1.0% mIoU) on dense prediction tasks. The training code could be found at https://github.com/xiaolai-sqlai/RaMLP.