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Recently, one of the most frequent natural hazards around several regions in the world is the landslide events. The area of Jabal Farasan in the northwest Jeddah of Saudi Arabia suffers from landslide events. The main cause of these events was identified due to the anthropogenic activities represented by mining activities. In this work, different machine learning algorithms (MLA), Random Forest (RF), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) were implemented to predict the landslides events integrated with remote sensing data. The objective is to generate landslides susceptibility prediction map using different MLA. The landslides inventory map was prepared in the present study based on the historical landslide’s events. Landslides at 1354 locations were used to train the models and validate the prediction. Landslides controlling factors include: elevation, slope, curvature, aspect, distance from roads …