Forest reserves are increasingly threatened by anthropogenic activities and climate-induced environmental change, necessitating robust geospatial techniques for effective monitoring and sustainable management. This study evaluated the long-term dynamics and conservation status of the Shere Hills Forest Reserve (SHFR), Plateau State, Nigeria, using an integrated Geographic Information System (GIS) and Remote Sensing framework. Multi-temporal Landsat imagery (1994, 2004, 2014, and 2024), high-resolution unmanned aerial vehicle (UAV) imagery, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data, and Differential Global Positioning System (DGPS) observations were integrated to assess land use/land cover (LULC) dynamics, vegetation condition, degradation patterns, and forest fragmentation. Random Forest Classification was applied for LULC mapping, while the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), change detection, and landscape fragmentation metrics were used to quantify vegetation health and ecological integrity. Classification results demonstrated high reliability, with overall accuracies ranging from 84.8% to 92.8% and Kappa coefficients between 0.808 and 0.897. The analysis revealed substantial landscape transformation over the 30-year period, characterized by a 177% increase in built-up areas and a 29% decline in cropland, reflecting rapid urban expansion and changing land-use practices. In contrast, forest cover increased by 4.3%, accompanied by improvements in NDVI, VCI, core forest connectivity, and reduced edge density, indicating ecosystem resilience and progressive natural regeneration despite increasing anthropogenic pressure. These findings demonstrate the value of integrating GIS and Remote Sensing for long-term forest monitoring and provide a scientific basis for evidence-based conservation planning, biodiversity protection, and sustainable management of the Shere Hills Forest Reserve and similar montane forest ecosystems.