Globally, groundwater dependent ecosystems (GDEs) are increasingly vulnerable to groundwater extraction and land use practices. Groundwater supports these ecosystems by providing inflow, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity that they support. Many aquatic systems receive groundwater as a portion of base flow, and in some systems (e.g., springs, seeps, fens) the connection with groundwater is significant and important to the system’s integrity and persistence. Groundwater management decisions for human use may not consider ecological effects of those actions on GDEs, which rely on groundwater to maintain ecological function. This disconnect between management and ecological needs can affect groundwater resources that have repercussions for both the GDEs and human populations that rely on them. This disparity can be attributed in part to a lack of information about where these systems are found and relationships with the surrounding landscape that may influence the environmental conditions and associated biodiversity. Knowledge of occurrence of GDEs in the northeastern United States is incomplete. As expanding urban areas alter the regional hydrology, threats to groundwater resources may increase. An objective of our research is to predict the occurrence of groundwater influenced ecosystems (GIEs) across the northeastern United States. We are applying geographically referenced information about known GIEs across two ecologically distinct EPA Level II Ecoregions (Atlantic Highlands, Mixed Woods Plains) in the northeastern United States using correlative distribution modeling methods [Generalized Linear Models (GLM), Generalized Additive Models (GAM), Maximum Entropy (MaxEnt), Random Forest] to produce a landscape scale habitat suitability maps for GIEs. We then evaluated the predictive outputs and ensemble model predictions to create consensus models for each Ecoregion. Knowledge of GIE locations and their contributing watersheds across this region can inform land management decisions, which can enhance conservation of these systems.