In order to accurately diagnose and track a variety of medical disorders, ultrasound imaging is essential. In the case of brachial flexes, nerve segmentation is a crucial duty for correct assessment. In this work, we provide a novel method for ultrasonic nerve segmentation in brachial flexes that makes use of recurrent neural networks (RNNs). The suggested approach entails creating a unique RNN architecture that draws inspiration from the U-Net framework in order to efficiently capture temporal and spatial relationships in the ultrasound picture sequences. Model training and assessment were conducted using a dataset that included manually identified nerve areas and ultrasound pictures of brachial flexes. Intersection over Union (IoU), dice coefficient, and pixel accuracy are examples of common segmentation metrics that were used to evaluate the effectiveness of the suggested method. Our experimental results reveal that RNNs are successful in catching the fine structural characteristics of nerves in ultrasound pictures, with promising segmentation accuracy. Furthermore, visual inspection qualitative analysis validates the model’s accuracy in defining nerve boundaries. All things considered, this study advances automated ultrasound nerve segmentation methods, which may find use in clinical settings to improve therapeutic and diagnostic approaches.