Background: Identifying the pituitary gland during surgery for pituitary neuroendocrine tumors (PitNET) is crucial for preserving gland tissue and reducing postoperative hormonal dysfunction. This study aimed to develop and validate a machine learning (ML) tool to identify the pituitary gland during endoscopic endonasal surgery. Methods: Anonymized surgical videos from PitNET resections were trimmed to key phases, starting after dura opening and ending before skull base reconstruction. Frames were manually annotated to delineate the pituitary gland’s location. The ML model’s performance was evaluated using a single hold-out set method. Results: A total of 2316 frames from 52 videos were annotated, with 60%, 20%, and 20% allocated to training, validating, and testing the ML model, respectively. Performance metrics were as follows: accuracy of 97.8%, specificity of 98.7%, recall of 27%, precision of 18.6%, and an F1-score of 0.22. Conclusions: This study highlights the feasibility of using ML to identify the pituitary gland in PitNET surgeries. While the model is highly accurate in distinguishing gland from non-gland tissue, its low precision indicates a propensity to misclassify adjacent background tissue as pituitary gland.Further refinements could enhance its precision, making it a valuable tool for improving intraoperative anatomical recognition and postoperative hormonal outcomes.