Various assessment schemes prioritize the identification of groundwater potential using spatial and remote sensing data, with the aim of improving people's lives. International agencies are exploring efficient locations for various agricultural sectors and domestic communities. The collection of data from various sources serves as an assessment parameter. In this observation framework, the integration of topography, geology, climatology, land use, and hydro-morphology data plays an essential role in simplifying groundwater modeling, saving time, and ensuring data accuracy by adopting the Analytic Hierarchy Process (AHP) assessment. In this chapter, our research aims to integrate multi-source spatial data and Sentinel-2 remote sensing data in 2023 at 10-m resolution to model groundwater in relation to the forest area status in the Tropical Antokan Watershed, Indonesia. Ten thematic layers of influencing factors will be applied, such as geology, geomorphology, soil type, soil texture, drainage density, slope, rainfall, flow direction, topographic wetness index, and land cover. The investigation results indicate that groundwater potential is divided into four categories: excellent (14.85%), good (27.15%), moderate (43.62%), and poor (14.39%). On the other hand, optimizing land use arrangements that represent the status of forest areas can cover 74.48% of the watershed, which can be converted into cultivation areas and play a significant role in local water control. Additionally, it is important to integrate remote sensing datasets into the area requirements of the investigated zone to reduce expenditure costs. Models derived from these observations can be used for a more optimal regulatory direction for sustainable groundwater management.