The Gluonic Excitations (GlueX) experiment is designed to search for exotic hybrid mesons produced in photoproduction reactions and to study the hybrid meson spectrum predicted from Lattice Quantum Chromodynamics. For the first time, the GlueX Central Drift Chamber was autonomously controlled using machine learning (ML) to calibrate in real time while recording cosmic ray tracks. We demonstrate the ability of a Gaussian Process to predict the gain correction calibration factor used to determine a high voltage setting that will stabilize the CDC gain in response to changing environmental conditions. We demonstrate the use of a data-driven method to calibrate a drift chamber via high-voltage control during an experiment in contrast to the traditional, computationally expensive method of calibrating raw data after data collection is complete.