Cryptococcus neoformans is a human pathogenic yeast that causes life-threatening infections especially in immunosuppressed patients. The environmental isolation of C.neoformans from Turkey was reported as early as 2004, although this was mostly from Eucalyptus camaldulensis colonization. Successful isolations were also reported from pomegranate (Punica granatum), oriental plane (Platanus orientalis), pine tree (Pinaceae), chestnut (Castanea sativa) and salt cedar (Tamarix hispida). The investigation of the relationship between the bioclimatic factors affecting the environmental isolation sites and the colonization of pathogens is a frequently used method. With this method, detailed risk maps can be generated in which environmental colonization can be estimated. The aim of this study was to use the high-resolution bioclimatic and previously-isolated yeasts' coordinates to create a valid model for the occurrence of C.neoformans in Turkey and provide insight into ecological processes. A machine learning approach using presence-only data software, maximum entropy (MaxEnt), was used to for the prediction of C.neoformans distribution. Climatic data and environmental bioclimatic variables from WorldClim were downloaded as 30 seconds spatial resolutions. The correlation between different Turkey bioclimatic layers were analyzed with ENMTools and similar layers were discarded. Forty-one different coordinates representing C.neoformans isolation points were used to generate a predictive map. The area under the curve and the omission rate were used to validate the model. Meanwhile, Jackknife tests were applied to enumerate the contribution of different environmental variables, and then to predict the final model. Maps were created using QGIS mapping software. In this study, we have shown that the coastal region of Anatolia, which is geographically located in the Northeastern Mediterranean Basin, as well as the entire Aegean region, carry an extremely high risk for the colonization of C.neoformans. Other areas which have not previously been reported for the isolation of C.neoformans were predicted to be potential colonization hotspots, including the western part of Ataturk Dam, the Amik Plain and the Bakircay and Gediz valleys. The maximum temperature of the warmest month, the mean temperature of the warmest quarter and the precipitation of the coldest quarter were the most important factors influencing the model's predictions. It was determined that the humidity in the environment affected the colonization especially in November. In conclusion, we produced a C.neoformans colonization risk map of Turkey for the first time. Obtaining more regional data will facilitate the identification of the regions having similar risk. This approach is useful for the clinical prediagnosis of cryptococcosis cases, which may be more common in places with environmental niches.