In the 21st century, global warming, erratic rainfall and the pressure on groundwater resources are becoming severe and leading to water scarcity, which contributes to the poverty of around one-third of the world's people. Hence, it became necessary to use new geospatial technologies for mapping and monitoring of groundwater resources. Spatial analysis of hydro-geological lineament network and hydrological modeling may allow accurate identification of areas that favor infiltration and recharge of overexploited groundwater. In Morocco, the Middle Atlas region suffers from a lack of reliable hydro-geological maps for local water resource managers. Consequently, this research aims to assess the relative contribution of the topo-hydrological factor, known as Height Above Nearest Drainage (HAND) for efficient exploration and management of groundwater resources in central Middle Atlas. The methodology was based on the Landsat-8 Operational Land Imager (OLI) data for retrieving the lineaments and the elevation data from Shuttle Radar Topography Mission (SRTM) for the calculation of the HAND model. Four landscape classes namely, waterlogged land, wetlands sloped, hillslope and plateaus, corresponding to the main hydrological systems, were delineated using the HAND model. In addition, fissured carbonate plateaus, faults, karsts, water sources, lakes and groundwater levels have been used to develop a decision support system based on geospatial criteria analysis. The fracture analysis indicated that the study area has many long and short hydro-geological lineaments, with the highest densities oriented NE–SW. Their synergy with the HAND model and the other factors has allowed the detection of potential areas for the recharge of the groundwater table. The results have been confirmed and validated using lakes, water sources and piezometric levels. The application of the HAND factor as an independent variable in the delineation of groundwater potential areas (GPAs) is demonstrated to be useful for improving the accuracy of the existing multi-criteria GIS models.