The Brachial Plexus (BP) nerves, responsible for upper limb functionality, extend from the spinal cord through the cervix coaxially canal to the armpit, and their precise localization is critical during neck surgeries to avoid potential damage. Misplacement of the catheter used for localization can lead to severe consequences like Erb’s Paralysis or extended patient recovery. An automated technique to locate these nerves aids in faster detection before surgery. Accurate ultrasound nerve segmentation using deep neural networks like U-Net, ResNet, and Inception ResNet V2 is crucial for enhancing regional anesthesia efficacy, reducing surgical risks, and expediting recovery. Employing transfer learning for training reduces time and computational expenses. The study evaluates the performance of these networks through metrics like Accuracy, Sensitivity, Specificity, Intersection over Union, and Dice Coefficient, utilizing ultrasound images from the Kaggle BP nerves dataset.