Land use and land cover (LULC) are basic input for planning and management at different administrative levels. LULC dynamics are significantly impacted by human population expansion, mobility, industrialization, and demand. Assessment of these anthropogenic activities in any area requires the analysis and prediction of LULC changes. In recent years, state of the techniques of LULC prediction based on MOLUSCE (Modules for Land Use Change Evaluation) plugin in QGIS (Quantum Geographic Information System) gained popularity. Consequently, this study employs the MOLUSCE plugin for LULC prediction. The Digital Elevation Model (DEM), aspect, slope, distance from rivers, and distance from roads, five spatial variables are input into the learning processes of the artificial neural network and cellular automata (ANN-CA) model within MOLUSCE to predict future LULC. This study performs predictions and analysis for the years 2027 and 2037 LULC for the Jagdalpur sub-division in the Bastar district of Chhattisgarh state of India. LULC maps from 2012, 2017, and 2022 are used as base maps. Using the 2012 and 2017 LULC maps along with spatial variable maps, the 2022 LULC map was predicted. In the validation phase, the predicted LULC for 2022 achieved 89.38% accuracy and an overall kappa of 0.795, indicating a high degree of prediction accuracy. LULC for 2027 and 2037 was predicted using subsequent LULC maps and spatial variables. The predicted LULC for the years 2027 and 2037 shows significant growth in cropland and built-up areas, with increases of 8.48% and 0.65% in 2027, and 14.67% and 1.32% in 2037, respectively. This study assists farmers and decision-makers in creating optimal land use plans and management strategies for the sustainable utilization of natural resources.