Machine learning-based image super-resolution is a robust approach for obtaining detailed bathymetric maps. However, in machine learning using supervised data, the dissimilarity in the features of training and target datasets degrades super-resolution performance. This study proposes a two-step method to generate training data with features similar to those of the target data using image transformation and composition. The super-resolution model trained via the proposed method on the Central Okinawa Trough data was applied to the bathymetry data around Okinotorishima Islands. The method improved the root mean squared error by up to 14.3% compared to conventional approaches, thus demonstrating the potential of combining artificial data generation with machine learning for super-resolution bathymetry mapping of the entire ocean floor.