Broad-crested　weirs　are　structures　used　to　measure　and　control　the　water　flows　in　rivers,　canals,　and　irrigation　and　drainage　networks.　Accurate　estimation　of　spillway　discharge　is　one　of　the　most　striking　elements　in　measurement　structures.　So　far,　many　researchers　have　studied　this　issue　based　on　various　experimental　conditions　and　a　specific　range　of　optional　variables.　They　also　have　presented　several　relations.　In　the　present　study,　113　data　sets　of　Bos　were　used　for　applicability　of　Artificial　Neural　Network　(ANN),　Gene　expression　programming　(GEP),　regression　models　to　estimate　the　discharge　coefficient　for　the　rectangular　broad-crested　weirs.　The　effectiveness　of　the　models　was　calculated　using　statistical　criteria,　including　the　coefficient　of　determination　(R2),　Root　Mean　Square　Error　(RMSE),　and　mean　absolute　error　(　MAE).　Comparing　the　models　showed　that　the　ANN　with　the　highest　R-2　coefficient　(0.9916),　lowest　RMSE　=　0.0012,　and　MAE　=　0.00052　has　the　best　discharge　coefficient　estimation　than　GEP　models,　regression　models,　and　other　empirical　relations　for　the　rectangular　broadcrested　weirs.