Studying the current range of species presence is crucial for ecologists and related scientists to understand potential habitats and the influence of environmental factors on species distribution. In this study, we used species distribution modeling (SDM) to look into where the yellow-bellied gecko, also known as the northern house gecko (Hemidactylus flaviviridis Rüppell, 1835), lives in Iran. We achieved this by combining four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Maximum Entropy (Maxent), and Generalized Linear Model (GLM). We utilized 19 historical bioclimatic variables, the Digital Elevation Model (DEM), slope, aspect, and the Normalized Difference Vegetation Index (NDVI). After calculating their correlation, we selected variables for modeling with a variance inflation factor (VIF) of less than 10. The findings indicate that the variables "Precipitation of the Coldest Quarter" (BIO19) and "Mean Temperature of Wettest Quarter" (BIO8) have the most significant influence on the species' distribution. The gecko primarily inhabits low elevations and slopes, particularly those below 400 meters above sea level with slopes less than 8 degrees, primarily in southern Iran. Additionally, we found that the NDVI had minimal impact on the distribution of the species. Therefore, we identify the provinces of Khuzestan, Bushehr, and Hormozgan, along with parts of the coastal strip of Sistan and Baluchistan, as suitable areas for the current presence of this species.