To understand the modeling challenges and to examine the important factors considered in Malayan sun bear (Helarctos malayanus) distribution studies, we reviewed 33 peer-reviewed articles published from 2003-2023. These studies used 54 environmental or anthropogenic variable types to investigate the distribution, habitat preference, and home range composition of sun bears. Most variable types are human disturbance (n=4), climate (n=3), topography (n=1), vegetation (n=11), or other ecological factors (n=3). Nevertheless, a number of rarely used variables might also be useful to include in future evaluations (i.e., food abundance), and observational evidence suggests that predator occurrence could also be informative. Importantly, no studies tested the performance of model prediction by using other presence points of the species in a similar or adjacent biogeographical area. In Myanmar, where the bear’s distribution is not well-known, we set up three annual surveys using 120 camera-trap stations in a portion of the Htamanthi Wildlife Sanctuary (HWS) in northern Myanmar during 2016-17 to 2018-19 to identify factors influencing bear distribution. From a total effort of 15,315 trap nights, we obtained 47 independent photo events of sun bears at 16%, 13%, and 9% of the stations each year. We analyzed eight factors potentially influencing the bear distribution and found that the top three ranked models were a combination of elevation, NDVI (Normalized Difference Vegetation Index), distance to water, and slope. The presence of tigers (Panthera tigris) in the area was found to have a positive relation with mean sun bear occupancy. In this study, we tested the prediction performance of the single-season occupancy model with another dataset. We tested the prediction performance of the top six models in the PresenceAbsence Package and calculated the AUC (Area under receiver curved), TSS (True skill statistics), and Kappa scores. The AUC score ranged from 0.5 to 0.6, while the TSS score ranged from -0.001 to 0.28. None of the top six models’ predictions perfectly agreed with the sanctuary-wide survey data. The discrepancies may be due to the limited sample size, the temporal scale of the prediction, and the presence of other ecological factors (e.g., predators, competitors, or food availability) not accounted for in the habitat use prediction. To improve the prediction performance of occupancy models, we recommend that future sun bear surveys increase the number and size of sampling efforts and include ecological covariates such as potential predators when possible.