Observing　the　ocean’s　interior　is　becoming　extremely　important　since　recent　evidence　suggests　widespread　warming　in　the　subsurface　and　deeper　ocean　as　a　response　to　the　Earth’s　Energy　Imbalance　(EEI)　in　recent　decades.　However,　the　ocean’s　interior　observations　are　sparse　and　insufficient,　severely　constraining　the　studies　of　ocean　interior　dynamics　and　variabilities.　Detecting　and　predicting　subsurface　and　deeper　ocean　thermohaline　structure　from　satellite　remote　sensing　measurements　is　quite　essential　for　understanding　ocean　interior　3D　environment　and　processes　effectively.　This　chapter　proposes　several　novel　approaches　based　on　artificial　intelligence　to　accurately　retrieve　and　predict　subsurface　thermohaline　structure　(upper　2000　m)　from　multiple　satellite　observations　combined　with　Argo　float　data.　We　manage　to　construct　AI-based　deep　ocean　remote　sensing　technique　with　high　spatiotemporal　applicability　to　subtly　detect　and　describe　global　subsurface　thermohaline　structure,　so　as　to　support　the　studies　of　ocean　internal　processes　and　anomalies　under　global　warming.　The　AI-based　approaches　demonstrate　great　potential　for　subsurface　environment　data　reconstruction　and　should　be　a　promising　technique　for　investigating　ocean　interior　change　and　variability　as　well　as　its　role　in　global　climate　change　from　satellite　remote　sensing　measurements.　©　The　Author(s)　2023.