Due to the relative scarcity of literature and the uniqueness of individual systems, making assignments of O NMR spectra for hydroxide-based systems can be challenging. As such, computational methods, in particular density functional theory (DFT), provide valuable tools in aiding assignments of the observed spectra. In this study, we first demonstrated static DFT results to establish the expected O NMR parameters for hydroxide-based CO 2 capture. However, the utility of these results is shown to be limited in this context and we thus stress the importance of including dynamic effects in computational NMR modelling for these systems. By training a machine learning force field (MLFF) on DFT data, we are able to run molecular dynamics at ab-initio accuracy with reduced computational cost for NMR parameter prediction. Fast dynamics on the time scale of nanoseconds are well described by molecular dynamics (MD) trajectories, however, slower motions have previously been required to be assessed by further NMR measurements or be parameterised by fitting of spectral data to infer dynamic processes. Notably, this often leads to continuum motions, e.g. molecular wobbling, being approximated by discrete hop models. In this work, we overcome this problem by directly combining MLFF-MD and stochastic intermediate exchange methods. We thus propose a novel approach for including dynamic NMR effects in fast, intermediate and combined regimes in a pre-determined -rather than empirically derived -fashion. This completes a universal approach for utilising machine-learning force fields for computational NMR prediction, across a range of chemical exchange regimes.