Random forest machine learning (ML) techniques have been used within a geographic information system (GIS) to develop regional scale landslide susceptibility maps for a local government area (LGA). To do this, a landslide inventory and database of known landslides are needed to be collected and curated, the various factors contributing to landslides identified, the data combined at a given scale, the random forest trained to the database of landslides, predictions made across the LGA, post processing and validation/sanity checking performed. The ML technique predicted landslide events with an acceptably high degree of accuracy. There are some residual uncertainties in the mapping which were considered to be no greater, and probably fewer, then if the mapping was done in the traditional way using geological reasoning.