The Western Himalayan biogeographic zone of India is characterized by highly fragile ecosystem, where land use and land cover (LULC) changes have impacted the environment as well as local biodiversity. This necessitates prediction of LULC changes to support sustainable land management practices. This study aims to address this need by projecting LULC and evaluating the changes anticipated for the year 2055, utilizing the MOLUSCE (Modules for Land Use Change Evaluation) plugin integrated into freely available QGIS software. MOLUSCE tool is used to simulate LULC change by incorporating predictive algorithms such as Artificial Neural Networks (ANN) and Weights of Evidence (WoE). In this study, decadal LULC data from 1975, 1985, 1995, 2005, and 2015 was extracted from reliable existing literature, including a recent study by (Rathore et al., 2022). The research is divided into two main stages: model calibration and model validation. For validation, LULC data from 1995 and 2005 were used to predict the LULC of 2015, and the model's output was then validated against the actual 2015 LULC data obtained from existing sources. The ANN-based approach showed an overall accuracy of 82% in this study, underscoring the model's effectiveness. The model was calibrated using LULC data from 1975 and 2015 to project the LULC scenario for the year 2055. This calibration accounts for historical trends and transitions, thereby improving the model's predictive accuracy for future scenarios. The study highlights the significance of using advanced geospatial tools like MOLUSCE in QGIS for LULC change modelling, especially in ecologically sensitive regions like the Western Himalayas. This study provides insights into potential future changes and contributes to ongoing efforts to preserve the ecological integrity of the Western Himalayan region.