Prediction of wildfire propagation plays a crucial role in reducing the impacts of such events. Various machine learning (ML) approaches, namely Support Vector Regression (SVR), Gaussian Process Regression (GPR), Regression Tree, and Neural Networks (NN), were used to understand their applicability in developing models to predict the rate of spread of grassfires. A dataset from both wildfires and experimental fires comprising 283 records with 7 features was compiled and utilized to develop and evaluate ML-based models. These models produced excellent fits to the model development dataset. Model fit against the evaluation dataset resulted in higher errors, with some of the models that yielded the lowest error against the model development dataset, producing the highest errors against the evaluation dataset. The predictive performance of the best ML-based models against that of operational models was evaluated. The SHAP visualization tool was used to determine the most influential variables in the best-performing models.