Invasive weed species (IWS) threaten ecosystems, the distribution of specific plant species, as well as agricultural productivity. Predicting the impact of climate change on the current and future distributions of these unwanted species forms an important category of ecological research. Our study investigated 32 globally important IWS to assess whether climate alteration may lead to spatial changes in the overlapping of specific IWS globally. We utilized the versatile species distribution model MaxEnt, coupled with Geographic Information Systems, to evaluate the potential alterations (gain/loss/static) in the number of potential ecoregion invasions by IWS, under four Representative Concentration Pathways, which differ in terms of predicted year of peak greenhouse gas emission. We based our projection on a forecast of climatic variables (extracted from <i>WorldClim</i>) from two global circulation models (CCSM4 and MIROC-ESM). Initially, we modeled current climatic suitability of habitat, individually for each of the 32 IWS, identifying those with a common spatial range of suitability. Thereafter, we modeled the suitability of all 32 species under the projected climate for 2050, incorporating each of the four Representative Concentration Pathways (2.6, 4.5, 6.0, and 8.5) in separate models, again examining the common spatial overlaps. The discrimination capacity and accuracy of the model were assessed for all 32 IWS individually, using the area under the curve and true skill statistic rate, with results averaging 0.87 and 0.75 respectively, indicating a high level of accuracy. Our final methodological step compared the extent of the overlaps and alterations under the current and future projected climates. Our results mainly predicted decrease on a global scale, in areas of habitat suitable for most IWS, under future climatic conditions, excluding European countries, northern Brazil, eastern US, and south-eastern Australia. The following should be considered when interpreting these results: there are many inherent assumptions and limitations in presence-only data of this type, as well as with the modeling techniques projecting climate conditions, and the envelopes themselves, such as scale and resolution mismatches, dispersal barriers, lack of documentation on potential disturbances, and unknown or unforeseen biotic interactions.