Effective pain management is a cornerstone of optimal perioperative care, significantly impacting patient recovery and outcomes. Regional anesthesia, particularly peripheral nerve blocks, plays a crucial role in achieving this by providing targeted analgesia. While ultrasound guidance has enhanced the precision of these procedures, challenges persist in accurately identifying nerve structures due to inherent image quality issues. Addressing these challenges is critical for improving the efficacy and safety of nerve blocks. Recent years have witnessed significant advances in medical image processing powered by deep learning, particularly in the segmentation of peripheral nerve blocks. This review summarizes current research progress and emerging techniques in this domain. We first introduce commonly used segmentation models, including Fully Convolutional Networks, U-Net and its variants,  and task-specific network architectures. We then examine the application of deep learning to the segmentation of upper and lower limb nerve blocks, highlighting improvements in accuracy and efficiency. Current limitations-such as challenges with data heterogeneity and model generalization-are critically analyzed, and future directions are proposed to enhance model robustness and clinical scalability. Ultimately, this paper underscores the potential of deep learning to revolutionize peripheral nerve block localization through automated and reliable image segmentation.