This study presents a proof-of-principle for using optical emission spectroscopy and artificial neural networks for real-time monitoring and control of the operational parameters of a Hall effect thruster: the anode voltage, the anode xenon injection, the discharge current, and the coil current. In that regard, we build an optical database of 26 spectral lines across 6469 operating conditions to train and test the neural network. We then reduced the learning lines from 26 to 15 based on their statistical correlation with the target parameters. After tuning the hyperparameters of the network, the network predicted the thruster’s parameters with notable accuracies: 95% for the anode voltage, 84% for the coil current, and 99% for both the anode flow rate and the discharge current. The estimated uncertainty of predictions, at 3σ, is ±51V for voltage, ±1A for coil current, ±0.15A for discharge current, and ±0.15mgs−1 for anode flow rate. The prediction calculations were within milliseconds and enabled real-time monitoring of the thruster parameters. Therefore, a proportional-integrator-derivative controller (PID) controller was implemented to regulate the anode voltage and flow rate based on the optical emission of the plume. The PID showcased short settling times from 0.1 to 0.4 s and overshoot levels up to 3% of the target value for the voltage and 10% of the target value for the flow rate. These results were for a fixed coil current at 4A. The study showed that changing the coil current may necessitate more sophisticated prediction models and control strategies. Future work will expand the model’s generalizability to different thruster types, propellants, and magnetic field configurations.