Somalilandsun- With wildlife populations in decline, understanding their distributions across the landscape are needed for management and conservation efforts, particularly in remote or hazardous regions. We used indigenous local knowledge to inform species distribution models (SDMs) to predict the distribution of 38 wildlife species historically documented in Somaliland, one of the most isolated, data-poor regions in Africa. We conducted 195 interviews with agro-pastoral men and women in 2016 and 2017 throughout Somaliland based on the environmental range captured by performing a Multivariate Environmental Simi- larity Surface (MESS) analysis. Respondents were asked if each species (1) occurred in the vicinity, and if they did, (2) whether it was common, rare, extirpated, or unknown. We tested two commonly used SDMs, Maxent and boosted regression trees (BRTs), to map the potential distribution of wildlife using interview data. We present detailed case studies of two species of high conservation priority: cheetah (Acinonyc juba- tus) and African wild ass (Equus africanus somaliensis). More than half of the respondents reported the pres- ence of 25 of the wildlife species in the survey. Additionally, we developed Maxent models for all surveyed wildlife known to develop a species richness map. The least reported species known to have inhabited Somaliland in recent decades were lion (Panthera leo), African wild ass, and Somali wild dog (Lycaon pictus somalicus). Surprisingly, 177 of respondents reported the presence of cheetah, which was presumed by many to be extirpated from Somaliland. Given the high number of reports, coupled with the BRT model results and evaluations, it may be that cheetahs persist throughout Somaliland. Unfortunately, our results also suggest that African wild ass may have been extirpated from Somaliland. Our models carry the uncer- tainties of the raw data, but provide habitat suitability maps to guide future targeted surveys. Our meth- ods and results demonstrate that integrating indigenous local knowledge with SDMs may be an essential tool for the conservation of wildlife in data-poor regions.