Bond　strength　between　fly　ash-based　concrete　(FGPC)　and　rebars　is　a　vital　index　for　ensuring　the　reliability　and　safety　of　reinforced　concrete　members.　Thus,　prediction　and　analysis　of　ultimate　bond　strength　between　FGPC　and　rebars　are　performed　based　on　machine　learning　data-driven　methods　in　this　paper.　A　latest　database　including　137　samples　from　the　published　papers　in　the　last　decade　is　established.　Four　machine　learning　algorithms　that　include　Lasso　regression,　Support　Vector　Regression　(SVR),　Random　Forest　(RF)　and　Extreme　Gradient　Boosting　(XGB)　are　adopt　for　the　construction　of　prediction　models,　and　model　evaluations　are　carried　out　by　using　four　metrics　(R2,　RMSE,　MAPE　and　MRE).　The　comparison　between　machine　learning　models　and　empirical　models　based　on　experiments　is　finished.　Besides　that,　the　feature　importance　is　analyzed　based　on　RF　and　XGB　models.　The　results　show　that　the　machine　learning　models　have　a　better　prediction　performance　than　the　empirical　models,　where　the　RF　and　XGB　models　are　optimal　estimators　with　R2　of　over　0.84　on　testing　set.　Lasso　and　SVR　models　possess　stronger　generalization　ability　than　RF　and　XGB　models.　The　relatively　important　influence　factors　are　compressive　strength　of　FGPC　(fc),　reactivity　modulus　of　precursors　(RM),　the　ratio　of　cover　thickness　to　diameter　of　rebar　(c/d)　and　the　ratio　of　anchorage　length　to　diameter　of　rebar　(l/d).　Increasing　slag　content,　fc,　RM,　and　c/d　are　beneficial　to　enhancement　of　bond　strength.　The　recommended　values　of　fc,　RM,　c/　d　and　l/d　are　50　MPa,　0.7,　4.5　and　5.0　respectively.