A hybrid nuclear data estimator (G-HyND) based on a machine learning technique with Gaussian processes (GP) was developed. G-HyND estimates cross-sections from a hybrid training dataset composed of an experimental dataset and an analytical dataset based on a nuclear physics model, and generates the cross-section datasets including the dataset's uncertainty information. It was demonstrated that an experimental dataset and a physics model-based analytical dataset perform a complementary role in nuclear data generation, and that the generated nuclear data from the hybrid training dataset are more reasonable than only those from the experimental dataset. Furthermore, possible solutions for two inherent GP problems, i.e. overfitting and computational cost, are presented within the G-HyND framework.