A statistical learning approach is presented to predict the dependency of steady hydrodynamic interactions of thin oblate spheroidal particles on particle orientation and Reynolds number. The conventional empirical correlations that approximate such dependencies are replaced by a neural-network-based correlation which can provide accurate predictions for high-dimensional input spaces occurring in flows with nonspherical particles. By performing resolved simulations of steady uniform flow at 1≤Re≤120 around a 1:10 spheroidal body, a database consisting of Reynolds number- and orientation-dependent drag, lift, and pitching torque acting on the particle is collected. A multilayer perceptron is trained and validated with the generated database. The performance of the neural network is tested in a point-particle simulation of the buoyancy-driven motion of a 1:10 disk. Our statistical approach outperforms existing empirical correlations in terms of accuracy. The agreement between the numerical results and the experimental observations prove the potential of the method.