The　use　of　remote　sensing　observation　to　estimate　subsurface　oceanic　variables,　including　subsurface　temperature　anomaly　(STA),　is　essential　for　the　study　of　ocean　dynamics　and　climate　change.　Here　we　report　a　new　method　that　combines　a　pre-clustering　process　and　a　neural　network　(NN)　approach　to　determine　the　STA　using　ocean　surface　temperature,　surface　height,　and　surface　wind　observation　data　at　the　global　scale.　Gridded　monthly　Argo　data　were　used　in　the　training　and　validation　procedures　of　the　method.　Results　show　that　the　pre-clustered　NN　method　was　better　than　the　same　method　without　clustering,　while　also　outperforming　a　clustered　linear　regressor　and　the　random　forest　method　recently　reported.　The　new　method　was　tested　over　a　wide　range　of　time　(all　months　from　2004　to　2010)　and　depth　(down　to　1900　m).　Overall,　our　best　estimation　resulted　in　an　overall　root-mean-squared　error　of　0.41　degrees　C　and　a　determination　coefficient　(R-2)　of　0.91　at　the　50　m　level　for　all　months.　The　R-2　decreased　to　0.51　at　300　m　but　was　still　better　than　the　calculation　without　pre-clustering.　This　method　can　be　expanded　to　estimate　other　key　oceanic　variables　and　provide　new　insights　in　understanding　the　climate　system.