The　prognosis　following　radical　surgery　for　intrahepatic　cholangiocarcinoma　(ICC)　is　poor,　and　optimal　follow-up　strategies　remain　unclear,　with　ongoing　debates　regarding　anatomic　resection　(AR)　versus　non-anatomic　resection　(NAR).　This　study　included　680　patients　from　five　hospitals,　comparing　a　combination　of　eight　feature　screening　methods　and　11　machine　learning　algorithms　to　predict　prognosis　and　construct　integrated　models.　These　models　were　assessed　using　nested　cross-validation　and　various　datasets,　benchmarked　against　TNM　stage　and　performance　status.　Evaluation　metrics　such　as　area　under　the　curve　(AUC)　were　applied.　Prognostic　models　incorporating　screened　features　showed　superior　performance　compared　to　unselected　models,　with　AR　emerging　as　a　key　variable.　Treatment　recommendation　models　for　surgical　approaches,　including　DeepSurv,　neural　network　multitask　logistic　regression　(N-MTLR),　and　Kernel　support　vector　machine　(SVM),　indicated　that　N-MTLR＇s　recommendations　were　associated　with　survival　benefits.　Additionally,　some　patients　identified　as　suitable　for　NAR　were　within　groups　previously　considered　for　AR.　In　conclusion,　three　robust　clinical　models　were　developed　to　predict　ICC　prognosis　and　optimize　surgical　decisions,　improving　patient　outcomes　and　supporting　shared　decision-making　for　patients　and　surgeons.