Here, we present a comparative study of thirteen machine learning models in predicting 19 F NMR chemical shifts given structural input data. Since different model families exhibit varying levels of applicability to this task due to distinct model architectures, this greatly influenced the specific models evaluated in our project. For instance, ensemble models are well suited for capturing intricate non-linear relationships, making them an attractive candidate. In contrast, simpler linear models may struggle with the complexity of the input data but are less prone to overfitting due to simpler parameters. Due to these factors, we selected models from these two major families in our approach.