This study examines the dynamics of land use and land cover (LULC) transformations over three decades, focusing on the years 1992, 2002, 2012, and 2022. The research emphasizes the importance of understanding these changes in the context of environmental and socio-economic impacts. Utilizing a Multilayer Perceptron Neural Network (MLP-NN) model, the study analyzes historical LULC data from satellite imagery to predict future changes. The MLP-NN model, with three layers (input, hidden, and output), was trained through 10,000 iterations and demonstrated impressive performance with a skill measure of 0.8724 and an accuracy rate of 89.08%. The model's accuracy was validated by comparing LULC estimates for 2022 with observed data. The study highlights the significant impact of factors such as elevation, distance from rivers, aspect, proximity to roads, and evidence likelihood on the model's performance. The research contributes to the understanding of LULC dynamics, aiding politicians, conservationists, and urban planners in making informed decisions and developing sustainable policies. The study also explores the effectiveness of the MLP-NN model in predicting LULC changes, emphasizing its potential for future applications in environmental research and management.This study examines the dynamics of land use and land cover (LULC) transformations over three decades, focusing on the years 1992, 2002, 2012, and 2022. The research emphasizes the importance of understanding these changes in the context of environmental and socio-economic impacts. Utilizing a Multilayer Perceptron Neural Network (MLP-NN) model, the study analyzes historical LULC data from satellite imagery to predict future changes. The MLP-NN model, with three layers (input, hidden, and output), was trained through 10,000 iterations and demonstrated impressive performance with a skill measure of 0.8724 and an accuracy rate of 89.08%. The model's accuracy was validated by comparing LULC estimates for 2022 with observed data. The study highlights the significant impact of factors such as elevation, distance from rivers, aspect, proximity to roads, and evidence likelihood on the model's performance. The research contributes to the understanding of LULC dynamics, aiding politicians, conservationists, and urban planners in making informed decisions and developing sustainable policies. The study also explores the effectiveness of the MLP-NN model in predicting LULC changes, emphasizing its potential for future applications in environmental research and management.