A universal attribute of species is that their distributions are limited by numerous factors that may be difficult to quantify. Furthermore, climate change-induced range shifts have been reported in many taxa, and understanding the implications of these shifts remains a priority and a challenge. One approach is to employ species distribution models which correlates species presence data with a set of predictor variables. Here, we use MAXENT to predict current suitable habitat and to project future distributions of two closely related Phymata species in light of anthropogenic climate change. Using species occurrence data from museum databases and environmental data from WorldClim, we identified environmental variables maintaining the distribution of Phymata americana and Phymata pennsylvanica, and created binary suitability maps of current distributions for both species on ArcMap. We then predicted future distributions using the same environmental variables under different Representative Concentration Pathways (RCP), created binary suitability maps for future distributions, and calculated the degree of overlap between the two species. We found that the strongest predictor to P. americana ranges was precipitation seasonality, while precipitation of the driest quarter and mean temperature of the coldest quarter were strong predictors of P. pennsylvanica ranges. Future ranges for P. americana are predicted to increase northwestward and southward at higher CO₂ concentrations. Suitable ranges for P. pennsylvanica are initially predicted to increase, but eventually decrease with slight fluctuations around range edges. There is an increase in overlapping ranges in all future predictions. These differences in optima provide evidence for different environmental requirements for P. americana and P. pennsylvanica, accounting for their distinct ranges. Because these species are ecologically similar and can hybridize, climate change has potentially important eco-evolutionary ramifications. Overall our results are consistent with effects of climate change that is highly variable across species, geographic regions and over time.