We present the use of artificial neural networks ͑ANNs͒ to model an electromagnetic microelectromechanical system ͑MEMS͒ microactuator. It is inherently complex and time consuming to model/ predict the response of an electromagnetic microactuator numerically by finite element analysis, particularly when it is actuated by a pulse of current in media with different properties ͑e.g., air, water, and diluted methanol͒. ANNs are used to model the maximum displacement ͑d max ͒ of the microactuator for a range of burst frequencies ͑f b ͒ and input currents ͑I coil ͒, as well as different mechanical designs and actuation media. The prediction errors of the ANN model in normal and pressurized air are Ͻ13 and Ͻ2%, respectively. The prediction error for the same response in water or 50% diluted methanol in water is Ͻ10%.