Density Functional Theory (DFT) is an ab initio computational modeling method applying quantum mechanical principles to accurately describe the distribution of electrons within molecules, enabling the precise predictions of electronic molecular structures . DFT calculates the shielding effects experienced by nuclei due to surrounding electron densities to determine NMR chemical shifts . However, the computational expense of such calculations can prove prohibitive for researchers, limiting practicality when handling large datasets . In order to address this, machine learning techniques have been employed to generate similar predictive values at a fraction of the compute cost. This provides an accelerated shortcut for the normalized process while maintaining a reasonable balance between speed and accuracy , allowing for the prediction of chemical properties of previously unknown structures. Other advanced modeling techniques have also been recently introduced in the forms of graph neural networks (GNN) to predict 19 F and 13 C chemical shifts by respectively . While many of these models rely on one-dimensional (e.g. SMILES) or two-dimensional (e.g. graphs) chemical representations, they do not contain enough information given the inherent three-dimensional nature of anisotropic and bond-polarization effects that contribute to NMR shielding constants. To address this, we postulate that the simplest representation of each fluorine's local electronic environment can be gathered through encoding three-dimensional structural information of proximal neighboring atoms along with electronegativity and atomic weight. Due to the 3D nature of this input data, machine learning models trained using this dataset will be able to capture local atomic environments more effectively than the aforementioned 2D or 1D representations. Specifically, these models will have the ability to form crucial connections paralleling real time chemical interactions from the information offered through this third dimension within our data. This featural input can then be used to train machine learning models to predict DFT-calculated 19 F NMR chemical shifts at a fraction of the computational cost associated with a full DFT calculation. file format and subsequently encoded. Optimal distance for input data was then determined after model evaluation and testing based on runtime speed, MAE, and R 2 . Upon running all the models using the dataset tailored to this optimal atomic radius, we were then able to determine the best performer.