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The conservation of threatened and rare species in remote areas often presents two challenges: there may be unknown populations that have not yet been documented and there is a need to identify suitable habitat to translocate individuals and help populations recover. This is the case of the reticulated giraffe (Giraffa reticulata), a species of high conservation priority for which: (a) there may be unknown populations in remote areas, and (b) detailed maps of suitable habitat available within its range are lacking. We implemented a species distribution modeling (SDM) workflow in Google Earth Engine, combining GPS telemetry data of 31 reticulated giraffe with Landsat 8 OLI, Advanced Land Observing Satellite Phased Arrayed L‐band Synthetic Aperture Radar, and surface ruggedness layers to predict suitable habitat at 30‐m spatial resolution across the potential range of the species. Models had high predictive power, with a mean AUC‐PR of 0.88 (SD: 0.02; range: 0.86–0.91), mean sensitivity of 0.85 (SD: 0.04; range: 0.80–0.91), and mean precision was 0.81 (SD: 0.02; range: 0.79–0.83). Model predictions were also consistent with two independent validation datasets, with higher predicted suitable habitat values at known occurrence locations than at a random set of locations (P < 0.01). Our model predicted a total of 5519 km2 of potentially suitable habitat in Kenya, 963 km2 in Ethiopia, and 147 km2 in Somalia. Our results indicate that is possible to combine moderate spatial resolution imagery with telemetry data to guide conservation programs of threatened terrestrial species. We provide a free web app where managers can visualize and interact with the 30 m resolution map to help guide future surveys to search for existing populations and to inform future reintroduction assessments. We present all analysis code as a framework that could be adapted for other species across the globe. [ABSTRACT FROM AUTHOR]