The Kedarnath Wildlife Sanctuary, situated in Uttarakhand, India, is a critical hub of ecological diversity and ecosystem services. However, rapid land use and land cover (LULC) transitions in the region have raised concerns regarding the sustainability of these services. This chapter presents a holistic assessment and quantification of ecosystem services in response to the dynamic trajectory of LULC diversions within the sanctuary covering a longitudinal period of over two decades (2001–2024). Landsat images were imported and classified into seven classes, namely forests, rangelands, cropland, waterbody, settlements, bare land and snow cover using random forest (RF) machine learning-based classifier in Google Earth Engine (GEE) for the year 2001, 2014 and 2024. Classified LULC maps were then utilised to determine the ecosystem service value (ESV) of the sanctuary deploying benefit transfer method (BTM). By combining the remote sensing data, machine learning techniques and mathematical calculations, this study investigates the spatiotemporal patterns of land use conversion and their consequences on total ESV. The results of this study indicate that forests LULC class has the highest contribution in total ESV; however, it has shown declining trend over the stipulated years. The total economic value of ecosystem service also reflects decreasing pattern from ~134.09 million dollars (in 2001) to ~113.68 million dollars (in 2024). These results highlight the critical necessity for proactive conservation actions and sustainable land management approaches. These insights can inform decision-making processes aimed at striking a balance between development imperatives and ecological sustainability in ecologically sensitive regions like the Kedarnath Wildlife Sanctuary.