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Void fraction plays a vital role in diverse industries like oil, petrochemical, etc., which involve a wide range of fluids, including two-phase and three-phase fluids. Various procedures are used for the measurement of the void fraction, with one of the most popular being capacitance-based sensors. The characteristics of the fluid inside the pipe affect the output of this type of sensor, and every property, such as temperature, pressure, and density, plays a role. This paper presents the use of an Artificial Neural Network (ANN) and a combined capacitance-based sensor to measure the void fraction of a two-phase air-water homogeneous fluid. The aim is to develop a system that can predict void fraction independent of temperature and pressure changes. To achieve this, the COMSOL Multiphysics software was used to design and simulate two widely used sensors, concave and ring, to create a combined capacitance sensor …