In　this　study,　the　effects　of　processing　parameters　on　the　geometry　of　femtosecond　laser　fabricated　microgrooves　on　4H-SiC　wafer　were　investigated.　To　achieve　a　fast,　accurate　and　intelligent　manufacturing　process,　response　surface　methodology　(RSM)　and　artificial　neural　network　(ANN)　method　were　applied　for　modeling　and　predicting　the　geometric　features　of　microgrooves.　In　addition,　analysis　of　variance　(ANOVA)　was　employed　to　determine　the　most　significant　input　variables　on　the　responses.　The　RSM　result　showed　that　the　third-order　polynomial　model　equation　was　in　high　accordance　with　the　experi-ments.　During　the　analysis,　laser　power,　scanning　speed,　and　scanning　times　were　set　as　input　variables,　and　the　depth,　width　and　surface　roughness　(Ra)　were　set　as　response/　output　variables.　Finally,　the　predictive　capabilities　of　the　RSM　and　ANN　models　were　compared　with　each　other　in　terms　of　coefficient　of　determine　(R-2),　root　mean　squared　error　(RMSE),　and　relatively　error　(RE).　The　results　indicated　that　the　ANN　method　have　a　higher　accuracy　compared　to　those　of　the　RSM　model.　(C)&　nbsp;2022　The　Author(s).　Published　by　Elsevier　B.V.&　nbsp;&　nbsp;