Mamba-based architectures have shown promising performance in medical image segmentation. Accurate segmentation demands effective capture and integration of both global context and local details. However, existing methods often lack a balanced approach to extracting and fusing global and local information within the encoder and decoder. To address this issue, we introduce Global-Local Vision-Mamba with Semantic Fusion Network (GLM-SFNet), which is designed for balanced global-local feature processing in medical image segmentation. In the encoder, GLM-SFNet employs a Local-Global Vision State Space block (LGVSS). LGVSS strategically integrates four-directional scanning Mamba to capture comprehensive global context while incorporating Learnable Descriptive Convolution (LDC) to ensure detailed local feature extraction. For the decoder, we propose a Semantic Fusion Decoder (SFD), which achieves enhanced information integration and boundary precision by strategically combining global and local semantic fusion modules. Extensive experiments on three benchmark datasets demonstrate that GLM-SFNet achieves state-of-the-art segmentation performance while maintaining a lightweight architecture.