Critical　heat　flux(CHF)　is　a　significant　parameter　that　determines　the　heat　transfer　capability　of　nuclear　reactors,　and　therefore　prediction　of　CHF　with　accuracy　is　of　great　importance　for　the　design　and　safety　analysis　of　nuclear　power　plants　(NPPs).　This　paper　presents　a　novel　hybrid　model　based　on　Gaussian　process　regression　(GPR)　and　ant　colony　optimization　(ACO)　for　the　prediction　of　CHF.　In　this　model,　the　ACO　algorithm　is　employed　to　optimize　the　hyper-parameters　of　GPR　based　on　a　training　set　derived　from　two　published　literature　sources.　Prediction　of　CHF　is　performed　under　three　conditions:　fixed　inlet　condition,　local　condition,　and　fixed　outlet　condition.　The　predicted　results　of　the　hybrid　model　are　compared　to　those　of　support　vector　regression　(SVR).　It　is　shown　that　this　hybrid　model　is　superior　to　SVR　in　terms　of　lower　prediction　errors.　The　parametric　trends　of　CHF　are　also　analyzed　to　evaluate　the　prediction　performance　of　both　models.　The　predicted　values　of　the　hybrid　model　have　a　closer　agreement　with　experimental　values　than　SVR.　It　is　observed　as　well　that　the　presented　model　can　favorably　improve　the　prediction　precision　of　CHF　and　has　excellent　potential　for　other　related　applications　in　nuclear　engineering.　©　2020　The　Author(s)