Estimating　the　ocean　mixed　layer　depth　(MLD)　is　crucial　for　studying　the　atmosphere-ocean　interaction　and　global　climate　change.　Satellite　observations　can　accurately　estimate　the　MLD　over　large　scales,　effectively　overcoming　the　limitation　of　sparse　in　situ　observations　and　reducing　uncertainty　caused　by　estimation　based　on　in　situ　and　reanalysis　data.　However,　combining　multisource　satellite　observations　to　accurately　estimate　the　global　MLD　is　still　extremely　challenging.　This　study　proposed　a　novel　Residual　Convolutional　Gate　Recurrent　Unit　(ResConvGRU)　neural　networks,　to　accurately　estimate　global　MLD　along　with　multisource　remote　sensing　data　and　Argo　gridded　data.　With　the　inherent　spatiotemporal　nonlinearity　and　dependence　of　the　ocean　dynamic　process,　the　proposed　method　is　effective　in　spatiotemporal　feature　learning　by　considering　temporal　dependence　and　capturing　more　spatial　features　of　the　ocean　observation　data.　The　performance　metrics　show　that　the　proposed　ResConvGRU　outperforms　other　well-used　machine　learning　models,　with　a　global　determination　coefficient　(R2)　and　a　global　root　mean　squared　error　(RMSE)　of　0.886　and　17.83　m,　respectively.　Overall,　the　new　deep　learning　approach　proposed　is　more　robust　and　advantageous　in　data-driven　spatiotemporal　modeling　for　retrieving　ocean　MLD　at　the　global　scale,　and　significantly　improves　the　estimation　accuracy　of　MLD　from　remote　sensing　observations.