We report a novel method to separate metabolite and macromolecule signals from short-TE $$$^1$$$H-MRSI data using learned nonlinear low-dimensional models. A deep-learning-based strategy was developed to learn the nonlinear low-dimensional manifolds where the metabolite and MM signals reside, respectively. A constrained reconstruction formulation is proposed to incorporate the learned model as a prior to reconstruct and separate metabolite and MM signals. The performance of the proposed method was evaluated using both simulation and experimental short TE $$$^1$$$H-MRSI data. Promising results have been obtained, demonstrating the potential of the proposed method in addressing this challenging problem.