To formulate an effective growth management plan, it is imperative to comprehend the dynamic changes that transpire. This study focused on identifying such shifts spanning four decades, from 1990 to 2020, and utilized a GIS-integrated approach, employing Cellular Automata (CA)-Markov Chain model within TerrSet software for the MRBC area, to predict Land Use and Land Cover (LULC) for 2030. The classification method's accuracy evaluation yielded overall accuracy percentages of 94.11%, 94.11%, 90.19%, and 94.12% for 1990, 2000, 2010, and 2020, respectively, accompanied by Kappa values of 0.921, 0.921, 0.895, and 0.922. The LULC map for 2020 was forecasted and compared to the actual map for validation, revealing a discrepancy of less than 5% in class distribution. The study's findings indicated a 12.32% reduction in agricultural land (151.7 km2) compared to the 1990 LULC map in the projected 2030 map. In this future scenario, the converted region is allocated to urban and barren land classes. Consequently, decision-makers are urged to take necessary measures to preserve agricultural land from conversion, ensuring the enduring sustainability of agriculture.