Various macroscopic optical properties that are not observable in conventional homogeneous media may be realized in an optical metasurface by adjusting its sub-wavelength nanostructure. However, this requires precise and effective designing of structures. Therefore, systematic design methodologies for nanophotonic structures have garnered significant interest over the recent years. In this paper, we propose a deep-learning-based fast and efficient inverse design method for nanophotonic metasurface structures. A 10 × 10 plasmonic nanohole array structure perforated on an aluminum film was used to control both the amplitude and phase of the transmitted light with a high contrast using a small number of structural variables. To identify the structure that induces a desired field distribution, we constructed deep neural network (DNN) models that interconnected the structural variables of the plasmonic nanohole array with those of the field distributions. The DNNs were trained using data obtained via finite-difference time domain simulations. Moreover, we evaluated the performance of the proposed inverse design method for several targets, e.g., a rectangular grid with randomly determined intensities on different cells. The results confirmed an average cosine similarity of 0.86 for a field distribution at a focal length of 2,000 nm on a 4 × 4 grid with randomly determined intensities.