The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek’èhodì region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland mapping; our research helps fill this critical gap in the literature. A machine-learning, object-based approach was employed to classify the fused data stacks and included both mean and standard deviation image-object feature extractions. In this study, 18 random forest models were tested, each including various sensor inputs and feature extractions. The highest accuracy was achieved using a fusion of optical, radar, and ArcticDEM data and included both the mean and standard deviation of image objects (88.17% overall accuracy and kappa 0.858). Vegetated wetlands had producer accuracies ranging from 74% to 86%, whereas open water was 92%. Feature importance rankings indicated that 16 of the top 20 variables were derived from optical data, three from radar, and one from the ArcticDEM. The results of our study will be used to assist governments and other interested parties in advancing conservation initiatives for this significant high-latitude region.