Predicting species' suitable habitats is critical to biodiversity conservation planning and implementation. Species habitat distribution is closely linked to environmental and bioclimatic variables which are widely used for estimating habitat suitability (HS) from species distribution models (SDMs). Integration of environmental parameters derived from satellite remote sensing, bioclimatic variables, and edaphic properties has created an advanced way to improve the SDM performance. The objective of this study is to assess the performance for predicting the potential HS of the arid plant species using Maximum Entropy (MaxENT) species distribution model based on an ecological niche machine-learning algorithm. Prosopis cineraria (Ghaf) in the United Arab Emirates (UAE) was selected for the model simulation. The Ghaf tree is a keystone species to prevent desertification and improve soil fertility in arid environments. We have selected 33 environmental parameters, including satellite remote sensing data (MODIS NDVI, LST, and PET), WorldClim bioclimate variables and static edaphic properties (topography, elevation, soil quality) along with 100 field observations. Collinearity within the bioclimatic variables was eliminated using Pearson correlation. The variables with zero percentage contribution were eliminated for the final model simulation. To evaluate the contribution of environmental parameters to the performance of MaxEnt, we used three scenarios: a) All key predictor variables, b) Only bioclimatic variables, and c) without remote sensing variables. With scenario a) model simulation has substantially improved the potential HS prediction with mean AUC value 0.98, indicating a better predictive accuracy in the integration of satellite remote sensing data. MaxENT results showed that elevation, precipitation of coldest quarter, NDVI, and precipitation of warmest quarte had a significant contribution to the potential HS of Ghaf trees in the UAE. Model results showed that the spatial proportions of the potential HS in the UAE consisted of high (2%), medium (3.7%) and low (94.3%) suitability classes.