Earthquakes　threaten　people,　homes,　and　infrastructure.　Early　warning　systems　provide　prior　warning　of　oncoming　significant　shaking　to　decrease　seismic　risk　by　providing　location,　magnitude,　and　depth　information　of　the　event.　Their　usefulness　depends　on　how　soon　a　strong　shake　begins　after　the　warning.　In　this　article,　the　authors　implement　a　deep　learning　model　for　predicting　earthquakes.　This　model　is　based　on　a　graph　convolutional　neural　network　with　batch　normalization　and　attention　mechanism　techniques　that　can　successfully　predict　the　depth　and　magnitude　of　an　earthquake　event　at　any　number　of　seismic　stations　in　any　number　of　locations.　After　preprocessing　the　waveform　data,　CNN　extracts　the　feature　map.　Attention　mechanism　is　used　to　focus　on　important　features.　The　batch　normalization　technique　takes　place　in　batches　for　stable　and　faster　training　of　the　model　by　adding　an　extra　layer.　GNN　with　extracted　features　and　event　location　information　predicts　the　event　information　accurately.　We　test　the　proposed　model　on　two　datasets　from　Japan　and　Alaska,　which　have　different　seismic　dynamics.　The　proposed　model　achieves　2.8　and　4.0　RMSE　values　in　Alaska　and　Japan　for　magnitude　prediction,　and　2.87　and　2.66　RMSE　values　for　depth　prediction.　Low　RMSE　values　show　that　the　proposed　model　significantly　outperforms　the　three　baseline　models　on　both　datasets　to　provide　an　accurate　estimation　of　the　depth　and　magnitude　of　small,　medium,　and　large-magnitude　events.