Groundwater in coastal regions is threatened by saltwater intrusion (SWI). Beach nourishment is used in this study to manage SWI in the Biscayne aquifer, Florida, USA, using a 3D SEAWAT model nourishment considering the future sea level rise and freshwater over-pumping. The present study focused on the development and comparative evaluation of seven machine learning (ML) models, i.e., additive regression (AR), support vector machine (SVM), reduced error pruning tree (REPTree), Bagging, random subspace (RSS), random forest (RF), artificial neural network (ANN) to predict the SWI using beach nourishment. The performance of ML models was assessed using statistical indicators such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), means absolute error (MAE), root mean square error (RMSE), and root relative squared error (RRSE) along with the graphical inspection (i.e., Radar and Taylor diagram). The findings indicate that applying SVM, Bagging, RSS, and RF models has great potential in predicting the SWI values with limited data in the study area. The RF model emerged as the best fit and closely matched observed values; it obtained R2 (0.999), NSE (0.999), MAE (0.324), RRSE (0.209), and RMSE (0.416) during the testing process. The present study concludes that the RF model could be a valuable tool for accurate predictions of SWI and effective water management in coastal areas.