Land use–land cover (LULC) is crucial for analyzing land-use patterns and helping prediction future sustainable land management. LULC change detection is crucial for effective environmental management and urban planning. This study employs QGIS and the multilevel land-use change evaluation (MOLUSCE) plugin to analyze LULC changes in Wayanad, district of Kerala, South India, for a period of 2017 and 2023. The study area, characterized by diverse land-use patterns, underwent significant transformations due to urbanization and land development. We evaluate changes (shifts) in seven different kind of land cover/use, including water, trees, rangeland, flooded (overflowed) vegetation, crops, built area, bare/uncover land, etc., by integrating remote sensing data and spatial analysis techniques. The cellular automata–artificial neural network (CA-ANN) was utilized to quantify the extent and nature of these changes. The result indicates that substantial increase of 8.02% in built areas and a 3.57% increase in crops of the total area of Wayanad district at the expense of 12.73% trees (forest land). The findings underscore the importance of continuous monitoring and provide a valuable framework for policymakers to develop sustainable land-use strategies. This research demonstrates the effectiveness of the QGIS and MOLUSCE plugin with multilayer perceptron (MLP) as tools for comprehensively understanding and managing land-use dynamics.