Objective: Determine current and potential distribution of S. tacaco in Costa Rica with seven Species Distribution Models (SDM), in order to optimize the management of S. tacaco genetic resources, aimed at identifying patterns of geographic distribution and possible climatic adaptations allowing to have perspectives on their conservation and genetic breeding. Design/Methodology/Approach: 21 points of occurrence together with 19 bioclimatic variables and altitude were used to evaluate seven machine learning models and an assembly of these. Open-source libraries running in Rstudio were used. Results: Distribution models were inferred by the variables bio1, bio2, bio3, bio4, bio12, bio13, bio14, bio18 y bio19. The generalized additive model obtained the highest values ??of area under the curve (0.96) and True skill statistic (0.90), however, the seven models tested and the assembly showed adequate performance (AUC> 0.5 and TSS> 0.4). Bioclimatic variables related to temperature were the ones with the greatest contribution to the models and the main limitations in the distribution of S. tacaco. Study limitations/implications: Possibly a greater number of occurrence points are required to evaluate distribution models. Findings/Conclusions. Areas with high potential distribution suitability for S. tacaco are found in central valleys of Costa Rica, covering regions of the provinces of Alajuela, Cartago, San José and Puntarenas. These areas can be sources of germplasm for future conservation and breeding studies.