Firebrands, or hot embers, play a major role in the spread of wildfires, especially near the wildland–urban interface (WUI). They can travel long distances and ignite new fires far from the main fire source. In this study, we examine the application of machine learning (ML) models to predict the firebrand generation time (FGT). The dataset on firebrands was sourced from the US Forest Service website generated in a test campaign (varying species, fuel size, wind, heat intensity) conducted by the CIRE laboratory at Oregon State University in 2018. We evaluated five ML models—Support Vector Machine (SVM), Linear Regression (LR), Multi-layer Perceptron (MLP), Random Forest (RF), and XGBoost Regressor (XGR) with a dataset size of 187 observations and 6 features for each row. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE) were calculated to compare models’ performance. Also, SHAP visualization tool was implemented to provide insights into model predictions and to identify the most influential features in the best performing model. It was observed that the experimental results and the results obtained through ML models are in sync with each other, and therefore, ML techniques can be utilized for dealing with wildfire-related problems.