In this chapter, we analyze the model of estimating the probability of occurrence of landslide in areas that contain crucial infrastructures in Karnataka’s Ghats area using integrated databases and machine learning algorithms. Landslide data are then merged with the aspects such as aspect, curvature, earthquakes, elevation, flow, lithology, normalized difference vegetation index, normalized difference water index, plan curvature, precipitation, profile curvature, slope, temperature, humidity, rainfall, moisture, and pressure. Predicting the possibility of a landslide entails the application of Random Forest, the K-Nearest Neighbor (KNN) Regression, Logistic Regression, and Decision Tree. The evaluation is then done by statistical measures of accuracy, precision, F1-score, and recall score. The analyses prove that the best model is the Random Forest with the mutual information method for selecting the features. The envisaged model of landslide prediction looks promising and appears to be highly reliable in its capacity to predict likelihood of a landslide occurring with the help of selected machine learning algorithms.