The hydrocyclone is one of the most widely used industrial devices for separation of particles. The main objective of this article is to build a generalized neural network-based model for describing cyclones in laboratory and industrial environments and unusual configurations, covering a wide range of pressures and flow rates, angles, and lengths of cyclone nozzle. A wide range of parameters were investigated in laboratory-scale cyclones and used for training networks for final accurate estimations. A parallel neural network (NN) model was developed for finding different parameters' effects on efficiency and other possible expected results. Our tests show that parallel processing provides faster and more accurate results than simple NNs. The results show that significant efficiency improvement comes with length increments. Also, efficiency is strongly affected by the geometry parameter and feed condition.