The present work has been carried out to investigate the effect of process variables on gas holdup and develop an empirical equation and a neural network model for online process control of the gas holdup based on the operating variables. In this study, the effect of process variables (nozzle diameter, circulation pressure, aeration rate, and frother dosage) on gas holdup in a cyclonestatic micro-bubble flotation column of an air/oily wastewater system was investigated. Gas holdup was estimated using a pressure difference method and an empirical equation was proposed to predict gas holdup. A general regression neural network (GRNN) model was also introduced to predict gas holdup for the cyclone-static micro-bubble flotation column. The predictions from the empirical equation and the GRNN are in good agreement with the experiment data for gas holdup, while the GRNN provides higher accuracy and stability compared with that of the empirical equation.