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Operating room procedure duration is central to scheduling efficiency, resource utilisation, staff coordination, and perioperative safety. Inaccurate forecasts can create idle capacity, overtime, delayed starts, cancellations, and avoidable strain across surgical services. Conventional estimates often rely on surgeon judgment, historical averages, or static regression models. These approaches do not fully account for evolving intraoperative conditions or the temporal structure of surgical progress once a case has begun. This article develops a conceptual temporal neural network for forecasting procedure duration before incision and updating the expected completion time during surgery. The model is designed to combine static case information with sequential intraoperative event logs. The proposed architecture uses procedure codes, surgeon-specific historical performance, anesthesia records, patient comorbidity profiles, and timestamped operative events. A recurrent neural network based on LSTM or GRU principles would update a remaining-time distribution as new events occur. Conceptually, the model could provide an initial duration estimate and then refine that estimate as the case progresses. For example, it would be expected to revise completion time upward when a laparoscopic case converts to an open procedure or when unexpected bleeding is recorded. A temporal duration-forecasting model could improve operating room coordination, reduce avoidable waiting, and support more responsive perioperative decision-making. Its value would depend on careful validation, workflow integration, and transparent communication to clinical teams.