Natural disasters such as landslides are the source of immense damage to life and property. However, less is known on how one could generate accurate alerts against landslides sufficiently ahead in time. The major objective of this research is to develop and cross-validate a novel ensemble gradient boosting algorithm for generating specific warnings about impending movements of soil at a actual landslide site. Data about soil movements at 10-min intervals were collected via a landslide monitoring system deployed at a actual landslide site in real world situated at the Gharpa Hill, Mandi, India. A new ensemble support vector machine–extreme gradient boosting (SVM-XGBoost) algorithm was developed, where the alert predictions of an SVM algorithm were fed into an XGBoost classifier to predict the alert severity 10-min ahead of time. The performance of the SVM-XGBoost algorithm was compared to other algorithms including, Naïve Bayes (NB), decision trees (DTs), random forest (RF), SVMs, XGBoost, and different new XGBoost variants (NB-XGBoost, DT-XGBoost, and RF-XGBoost). Results revealed that the new SVM-XGBoost algorithm significantly outperformed the other algorithms incorrectly predicting soil movement alerts 10-min ahead of time. We highlight the utility of developing newer ensemble-based machine learning algorithms for an alert generation against impending landslides in the real world.