The accurate tracking and prediction of Land Use Land Change monitoring and prediction precision or accuracy are urgent for technological development and practical ecology management. This objective is pursued by understanding of the changing patterns of LULC temporally and spatially in Ningxia, China, a semi-arid region experiencing desertification and over-rapid urbanization. Therefore, one of the main objectives of this work is prediction for the future trends within this field of research. This research utilized the MODIS data taken from the satellite Moderate Resolution Imaging Spectroradiometer through remote sensing and advanced machine learning algorithms. During 2003 to 2023, the random forest classifier was used to monitor the LULC change in Ningxia, passing to different stage and increasingly affluent cities. These surveys predict the possible scenarios, including the rapid increase in desertification, urbanization side and erosion of the areas for agriculture, forestry and water-covered. A merging approach based on cellular automata and artificial neural network employed with the purpose of typing the future condition of land use and land cover. This approach relies on calibration to study future operations that remain unaffected by peoples’ choice. The calibrated version could be done to become tightly related to the spatial variables that heavily shift in time. The altered model achieved a great accuracy of 85% similarly the maximum value of 0.96 for the Kappa coefficient, which indicates the discovery of the ANN. Deserts and town are a few of the surfaces that have grown under science. As a result, wild animals’ facilities and natural flora have deteriorated, lowering agricultural output. (1) According to the transition probability matrix, this could be as high as 28% from agricultural land to desert. (2) One of the findings from spatial analytical trend analysis that I mentioned above, is also detected in terms of a distinct rise accumulation just for the desert category with respect to all other LULC types combined. (3) Thus, further increases in desert and artificial land might be coupled with urbanization (e. g.) to climate change.