This paper proposes a new method for accurately predicting rotating machine properties using a deep neural network (DNN). In this method, the magnetic field distribution over a cross-section of a rotating machine at a fixed mechanical angle is used as the input data for the DNN. The prediction accuracy of the torque properties of an inner permanent magnet (IPM) motor for the CNNs trained by the magnetic flux density distribution and material configuration is compared. It is shown that the proposed method facilitates a more accurate prediction of machine performance than a conventional method in which the cross-sectional image of a rotating machine is input to the DNN. Furthermore, the DNN learned by the proposed method is applied to the topology optimization algorithm. Topology optimization can be effectively accelerated because the number of analyses by the finite element method can be reduced using the proposed method. The total computing cost is reduced by 52.5% compared with conventional optimization without surrogate models.