Understanding the spatial and temporal distributions of rare species is important for informed management. The black-necked crane Grus nigricollis, a rare bird in China and flagship species in alpine wetlands, is nationally protected. Using site and environmental data, we use Maximum Entropy (MEM) and Random Forest (RFM) models to predict suitable habitat for this bird. Environmental variables that most affect its distribution are distance from buildings and roads, and isothermality, with contribution rates of 15.1%, 15.05%, and 5.85%, respectively. Using the AUC (area under curve) coefficient as an index to evaluate model precision, the predictions of the RFM are most consistent with the known distribution of these cranes, predicting the highest match with known crane sites (AUC = 0.945). Suitable habitat for this crane occurs mainly in Nyingchi city riverine wetland, where the centre of distribution is located in a valley where the Yarlung Tsangpo and Niyang rivers converge. A comparison between MEM and RFM outputs reduces uncertainty in predictions of black-necked crane spatial distribution, and filters optimal results for the distribution area. These results enable more-informed protection of habitat for this sacred crane.