This study focuses on the habitats of cetaceans in the Mid-Atlantic Bight, a region characterized by bathymetric diversity and the presence of distinct water masses (i.e. the shelf water, slope water, and Gulf Stream). The combination of these features contributes to the hydrographic complexity of the area, which furthermore influences biological productivity and potential prey available for cetaceans. The collection of cetacean sighting data together with physical oceanographic data can be used to examine cetacean habitat associations. Cetacean habitat modeling is a mechanism for predicting cetacean distribution patterns based on environmental variables such as bathymetric and physical properties, and for exploring the potential ecological implications that contribute to cetacean spatial distributions. We can advance conservation efforts of cetacean populations by expanding our knowledge of their habitats and distribution. Generalized additive models (GAMs) were developed to predict the spatial distribution patterns of sperm whales (Physeter macrocephalus), pilot whales (Globicephala spp.), bottlenose dolphins (Tursiops truncatus), and Atlantic spotted dolphins (Stenella frontalis) based on significant physical parameters along the continental shelf-break region in the Mid-Atlantic Bight. Data implemented in the GAMs were collected in the summer of 2006 aboard the NOAA R/V Gordon Gunter. These included visual cetacean survey data collected along with physical data at depth via expendable bathythermograph (XBT), and conductivity-temperature-depth (CTD) instrumentation. Additionally, continual surface data were collected via the ship’s flow through sensor system. Interpolations of physical data were created from collected point data using the inverse distant weighted method (IDW) to estimate the spatial distribution of physical data within the area of interest. Interpolated physical data, as well as bathymetric (bottom depth and slope) data were extracted to overlaid cetacean sightings, so that each sighting had an associated value for nine potentially significant physical habitat parameters. A grid containing 5x5 km grid cells was created over the study area and cetacean sightings along with the values for each associated habitat parameter were summarized in each grid cell. Redundant parameters were reduced, resulting in a full model containing temperature at 50 m depth, mixed layer depth, bottom depth, slope, surface temperature, and surface salinity. GAMs were fit for each species based on these six potentially significant parameters. The resultant fit models for each species predicted the number of individuals per km2 based on a unique combination of environmental parameters. Spatial prediction grids were created based on the significant habitat parameters for each species to illustrate the GAM outputs and to indicate predicted regions of high density. Predictions were consistent with observed sightings. Sperm whale distribution was predicted by a combination of depth, sea surface temperature, and sea surface salinity. The model for pilot whales included bottom slope, and temperature at 50 m depth. It also indicated that mixed layer depth, bottom depth and surface salinity contributed to group size. Similarly, temperature at 50 m depth was significant for Atlantic spotted dolphins. Predicted bottlenose dolphin distribution was determined by a combination of bottom slope, surface salinity, and temperature at 50 m depth, with mixed layer depth contributing to group size. Distribution is most likely a sign of prey availability and ecological implications can be drawn from the habitat parameters associated with each species. For example, regions of high slope can indicate zones of upwelling, enhanced vertical mixing and prey availability throughout the water column. Furthermore, surface temperature and salinity can be indicative of patchy zones of productivity where potential prey aggregations occur. The benefits of these models is that collected point data can be used to expand our knowledge of potential cetacean “hotspots” based on associations with physical parameters. Data collection for abundance estimates, higher resolution studies, and future habitat surveys can be adjusted based on these model predictions. Furthermore, predictive habitat models can be used to establish Marine Protected Areas with boundaries that adapt to dynamic oceanographic features reflecting potential cetacean mobility. This can be valuable for the advancement of cetacean conservation efforts and to limit potential vessel and fisheries interactions with cetaceans, which may pose a threat to the sustainability of cetacean populations.