Due　to　the　rapidly　increasing　number　of　base　stations　(BSs)　in　the　operational　cellular　networks,　their　energy　consumption　is　escalating.　In　this　paper,　we　propose　an　intelligent　data-driven　BS　sleeping　mechanism　relying　on　a　wireless　traffic　prediction　model　that　measures　the　BSs＇　capacity　in　different　regions.　Firstly,　a　spatio-temporal　cellular　traffic　prediction　model　is　proposed,　where　a　multi-graph　convolutional　network　(MGCN)　is　developed　to　capture　the　associated　spatial　features.　Furthermore,　a　multi-channel　long　short-term　memory　(LSTM)　solution　involving　hourly,　daily,　and　weekly　periodic　data　is　used　to　capture　the　relevant　temporal　features.　Secondly,　the　capacities　of　macro-cell　BSs　(MBSs)　and　small-cell　BSs　(SBSs)　having　different　environment　characteristics　are　modeled,　where　both　clustering　and　transfer　learning　algorithms　are　adopted　for　quantifying　the　traffic　supported　by　the　MBSs　and　SBSs.　Finally,　an　optimal　BS　sleeping　strategy　is　proposed　for　minimizing　the　network＇s　power　consumption.　Experimental　results　show　that　the　proposed　MGCN-LSTM　model　outperforms　the　existing　models　in　terms　of　its　cellular　traffic　prediction　accuracy,　and　the　proposed　BS　sleeping　strategy　using　an　approximated　non-linear　model　of　the　associated　capacity　function　achieves　near-maximal　energy-saving　at　a　modest　complexity.