Accurate prediction of land-use/land-cover (LULC) change is essential for sustainable urban and environmental planning. This study analyzes and models multi-decadal LULC dynamics in Shahrekord, Iran, using Landsat Surface Reflectance images for 1990, 2000, 2009, 2020, and 2024. Four classes were identified: Built-up/Barren (UB), Agriculture (AG), Water (WT), and Vegetation/Orchards (VG). LULC maps were produced using the Maximum Likelihood Classification (MLC) method in ArcGIS, based on pre-processed data from Google Earth Engine (GEE).Two predictive approaches were compared in TerrSet: Cellular Automata–Markov (CA-Markov) and Land Change Modeler (LCM) with a multilayer perceptron (MLP). Model performance was evaluated using hindcasts for 2020 and 2024, applying Overall Accuracy (OA), Kappa, and Pontius metrics. The CA-Markov model achieved higher accuracy and was therefore selected to predict LULC for 2030.Between 1990 and 2024, the UB class remained dominant, while AG increased in certain periods; WT and VG showed minor fluctuations. The findings confirm that neighborhood-based transitions drive most changes, enabling reliable short-term projections. The main limitations are the merged UB class and irregular time intervals. Recommendations for class refinement and temporal standardization are provided to improve future modeling and reproducibility.