Streamflow　prediction　is　a　challenging　task　due　to　the　different　processes　involved　in　streamflow　generation.　These　different　processes　have　different　characteristics　of　the　relationships　between　hydro-meteorological　variables　and　streamflow,　which　make　it　a　challenging　task　to　develop　single　data-driven　stream　flow　prediction　models　that　can　map　the　input-output　relationships　for　all　different　streamflow　regimes.　To　improve　the　performance　of　streamflow　prediction,　we　proposed　a　flow-regime-dependent　approach　to　map　the　relationships　between　hydro-meteorological　variables　and　streamflow　based　on　hydro-meteorological　condition　classification.　This　approach　integrates　the　least　absolute　shrinkage　and　selection　operator　(LASSO),　Fuzzy　C-means　(FCM)　and　Deep　Belief　Networks　(DBN)　and　therefore　referred　to　as　the　LASSO-FCM-DBN　approach.　This　approach　employs　LASSO　to　select　the　hydro-meteorological　variables　which　have　a　significant　impact　on　streamflow,　FCM　to　identify　different　streamflow　regimes,　and　DBN　as　a　data-driven　model　to　map　the　nonlinear　and　complex　relationships　between　the　selected　hydro-meteorological　variables　and　streamflow　within　different　flow　regimes.　To　assess　the　performance　of　the　proposed　approach,　two　comparative　studies　were　carried　out　-　1)　the　multivariable　FCM　was　compared　to　the　traditional　single-variable　threshold-based　method;　and　2)　the　performance　of　the　DBN　was　compared　to　a　traditional　Artificial　Neural　Networks　(ANNs)　model.　Two　stations　in　the　Tennessee　River,　USA　were　used　as　the　case　study.　The　results　demonstrate　that　the　performance　of　the　multivariable-based　FCM　classification　method　is　better　and　more　stable　than　the　traditional　threshold-based　single-variable　method,　due　to　the　sensitivity　of　the　single-variable　method　to　different　threshold　values.　In　addition,　DBNs　performed　better　than　traditional　ANNs　in　all　three　statistical　measures　considered.　Overall,　the　LASSO-FCM-DBN　multi-model　system　significantly　improved　the　performance　of　streamflow　prediction　and　is　therefore　a　valuable　tool　for　water　resources　management　and　planning.