Remote sensing analysis techniques have been investigated extensively, represented by a critical vision, and are used to advance our understanding of the impacts of climate change and variability on the environment. This study aims to find a means of analysis that relies on remote sensing techniques to demonstrate the effects of observed climate variability on Land Use and Land Cover (LULC) of the Mesopotamia region, defined as a historical region located in the Middle East. This study employed the combined analysis of the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and two statistical analysis methods (Pearson Correlation Analysis, r; Coefficient of Determination, R-2), which were applied using the Moderate Resolution Imaging Spectroradiometer data and observed surface meteorological data from 2000 to 2018. The resulting NDVI images show five LULC classes with NDVI values varying between -0.3 and 0.9. Furthermore, changes in the classified LULC area were compared statistically to those in NDVI values, where a positive relationship was found. Also, when the LST values and temperature are more extreme, the NDVI values were found to be smaller, suggesting a decrease in the density of vegetation cover. A negative correlation was found through Pearson correlation analysis (r = similar to-0.64), indicating a direct effect of increased temperatures on LULC. Indeed, this negative relationship between NDVI and LST was proven using R-2 values, where a two-dimensional scatter plot analysis showed that R-2 ranges from 0.54 to 0.9. Ultimately, the results obtained from this study reveal changes that may have many prominent effects in the field of LULC classification, accelerating the implications of climate change and variability factors.