This　study　employs　a　supervised　learning　method　to　predict　the　tunnel　boring　machine　(TBM)　penetration　rate　(PR)　with　high　accuracy.　To　this　end,　the　extreme　gradient　boosting　(XGBoost)　model　is　optimized　based　on　two　swarm　intelligence　algorithms,　i.e.,　the　sparrow　search　algorithm　(SSA)　and　the　whale　optimization　algorithm　(WOA).　Three　other　machine　learning　models,　including　random　forest　(RF),　support　vector　machine　(SVM),　and　artificial　neural　network　(ANN)　models,　are　also　developed　as　the　drawback.　A　database　created　in　Shenzhen　(China),　comprising　503　entries　and　featuring　10　input　variables　and　1　output　variable,　was　utilized　to　train　and　test　the　prediction　models.　The　model　development　results　indicate　that　the　use　of　SSA　and　WOA　has　the　potential　to　improve　the　XGBoost　model　performance　in　predicting　the　TBM　performance.　The　performance　evaluation　results　show　that　the　proposed　WOA-XGBoost　model　has　achieved　the　most　satisfactory　performance　by　resulting　in　the　most　reliable　prediction　accuracy　of　the　four　performance　indices.　This　research　serves　as　a　compelling　illustration　of　how　combined　approaches,　such　as　supervised　learning　methods　and　swarm　intelligence　algorithms,　can　enhance　TBM　prediction　performance　and　can　provide　a　reference　when　solving　other　related　engineering　problems.