Species distribution models (SDMs) are useful tools for future potential distribution patterns of species in the face of climate change. Turkey is expected to be affected considerably from climatic change i.e., up to 6°C increase in temperature and 50% decrease in precipitation by 2070. Therefore, there is an urgent need for conservation and management practices for future patterns of species. It is aimed current and future (using CMIP5 projected to 2070) potential distribution areas of Campanula lyrata Lam., which is formerly an endemic species. To do this, presence-only data was used, which is obtained from the Global Biodiversity Information Facility (GBIF). Bioclimatic data from was downloaded from WorldClim dataset with 10 km2 resolution. Species distribution modelling was performed using R program. Two regression techniques and two machine learning techniques namely Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), Support Vector Machine (SVM) and Random Forest (RF), were used, respectively. The bootstrapping method as partitioning resampling was also used for all analysis. Considerably high model performances as well as AUC values for all possible models were found. Significant range shifts between current and future climatic conditions were found. The most relevant relative importance variables were precipitation seasonality and precipitation of the wettest month. This study reveals the importance of the future distributional areas of species.Species distribution models (SDMs) are useful tools for future potential distribution patterns of species in the face of climate change. Turkey is expected to be affected considerably from climatic change i.e., up to 6°C increase in temperature and 50% decrease in precipitation by 2070. Therefore, there is an urgent need for conservation and management practices for future patterns of species. It is aimed current and future (using CMIP5 projected to 2070) potential distribution areas of Campanula lyrata Lam., which is formerly an endemic species. To do this, presence-only data was used, which is obtained from the Global Biodiversity Information Facility (GBIF). Bioclimatic data from was downloaded from WorldClim dataset with 10 km2 resolution. Species distribution modelling was performed using R program. Two regression techniques and two machine learning techniques namely Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), Support Vector Machine (SVM) and Random Forest (RF), were used, respectively. The bootstrapping method as partitioning resampling was also used for all analysis. Considerably high model performances as well as AUC values for all possible models were found. Significant range shifts between current and future climatic conditions were found. The most relevant relative importance variables were precipitation seasonality and precipitation of the wettest month. This study reveals the importance of the future distributional areas of species.