Based　on　the　nonlinear　relationship　between　multisource　marine　geodetic　data　and　seafloor　topography,　the　multilayer　perceptron　(MLP)　neural　network　is　introduced　into　bathymetry　prediction　to　improve　the　accuracy　of　bathymetry　model.　This　method　not　only　integrates　multisource　marine　geodetic　data,　but　also　takes　into　consideration　the　nonlinear　relationships　between　these　data　and　seafloor　topography.　Firstly,　we　utilize　terrain　information　and　the　multisource　marine　geodetic　data　[vertical　deflection,　gravity　anomaly,　vertical　gravity　gradient　(VGG),　mean　dynamic　topography　(MDT)]　around　the　shipborne　sounding　control　points　within　a　6　＇　x　6　＇　grid　as　input　data,　while　using　the　actual　bathymetry　at　control　points　as　output　data　to　train　the　MLP　neural　network　model.　Subsequently,　inputting　the　input　data　from　the　central　point　of　a　1　＇　x　1　＇　grid　within　the　study　area　into　the　MLP　model　to　predict　the　bathymetry　at　the　grid＇s　center.　Then,　based　on　the　predicted　bathymetry,　a　bathymetry　model　is　established　of　this　research　area.　Utilizing　this　methodology,　this　article　establishes　the　Gulf　of　Mexico　Bathymetric　Chart　of　the　Oceans　(MBCO1)　model.　Due　to　the　influence　of　complex　seafloor　topography　and　the　distribution　of　shipborne　bathymetry　points,　there　are　differences　in　training　and　prediction　among　different　regions.　To　address　this,　this　study　divides　the　research　area　into　five　subregions　(A,　B,　C,　D,　and　E)　and　establishes　bathymetry　model　(MBCO2　models)　through　each　sub-region.　Finally,　we　evaluated　the　accuracy　and　effectiveness　of　this　method　by　comparing　it　with　existing　bathymetry　models,　as　well　as　shipboard　depths.