NMR crystallography relies on state-of-the-art first-principles calculations such as density functional theory (DFT). Despite this advantage, the computational cost of DFT is relatively large for broad adoption. More importantly, the reliability of these calculations in predicting experimental parameters has to be assessed one isotope at a time, with the literature focusing on 1 H, C, Si, P and O in various systems. Literature benchmarks have provided large datasets of computed data available via community databases, such as in the Materials Project (materialsproject.org) and the Collaborative Computational Project for NMR (CCP-NC) that can be utilized for advanced machine learning (ML) studies to reduce the computational cost and democratize the adoption of NMR crystallography. The cubic scaling of DFT calculation time with respect to the number of valence electrons in the system limits these datasets to focusing on comparatively small unit cells of perfect crystalline materials modeled at a temperature of 0 K. Still, appropriately trained ML algorithms have demonstrated the ability to capture local geometry to predict δ iso with accuracy close to DFT while requiring only a fraction of computing time. While most of the machine learning efforts were focused on the prediction of δ iso , the experimentally measured isotropic chemical shift (or  iso , the DFT computed chemical shielding), comparatively, there have been relatively few studies that can demonstrate the ability of machine learning algorithms in predicting expressions of the electric field gradient (EFG) tensor parameters such as C Q for quadrupolar nuclei. NMR of quadrupolar species often results in complex lineshapes that must be decomposed in order to extract chemical insights from the connection between the structure and spectroscopic lineshape.