Species distribution modelling using a random forest (RF) machine learning approach was used to predict the probability of occurrence of sponges, sea pens, and large and small gorgonian corals in the Newfoundland and Labrador Region. A suite of 66 environmental predictor variables from different data sources were used. Models utilized catch records from the DFO multispecies trawl surveys, DFO/industry northern shrimp surveys and Spanish groundfish trawl surveys, DFO/industry northern shrimp surveys and Spanish groundfish trawl surveys. Most presece-absence models had good predictive capacity with cross validated Area Under the Reciever Operating Characteristic Curve (AUC) values ranging from 0.786 to 0.926. These models were used in a Canadian Science Advisory Secretariat (CSAS) process to delineate significant areas of cold-water corals and sponges in he Newfoundland and Labrador region.