Climate change and land use change jointly are the largest drivers of population declines, range contraction and extinction for many species across the globe. Wide‐ranging and large‐bodied species are especially vulnerable to habitat loss and fragmentation due to their typically low population densities, reflecting their need for extensive and connected habitats. We used the multi‐scale Random Forests machine learning algorithm to identify factors driving the habitat selection and future changes in habitat of Himalayan brown bear, an iconic wide‐ranging and large‐bodied species of high conservation interest, across a range of spatial scales. Habitat selection of brown bears was scale‐dependent, with most variables selected at broad scales. Climatic variables such as maximum temperature of coldest month, minimum temperature of warmest month and the potential evapotranspiration of wettest quarter strongly influenced habitat selection of brown bears. Future projections indicate a strong difference between the high and low emission scenarios. Alarmingly, our model suggests that high emission scenarios, with or without land use change, may result in a decline of brown bear habitat of >90% by the end of the century. In contrast, low emission scenarios are projected to reduce brown bear habitat by <23%, with much of the species range shifting to higher elevations. This study provides an integrative understanding of scale‐dependent variables in brown bear habitat selection, providing critical information for prioritizing areas for habitat management and conservation. Most importantly, our future projections imply that traditional conservation efforts, such as in situ conservation, will not be sufficient to protect the species without climate change mitigation. The incorporation of climate change mitigation and adaptation in conservation strategies will be one of the most pressing priorities in biodiversity conservation in this region.