This　research　introduces　an　innovative　resilient　design　framework,　addressing　gaps　in　building　performance　optimization　by　considering　a　holistic　life　cycle　perspective　and　factoring　in　climate　projection　uncertainties　from　the　2020s　to　2090s.　The　study　concludes　that　in　the　case　study,　future　climate　scenarios　in　different　Shared　Socio-Economic　Paths　significantly　impact　building　life　cycle　performance,　with　wall　U-value,　windows　U-value,　and　wall　density　identified　as　major　factors　affecting　building　performance　under　future　climate　uncertainty.　The　ensemble　learning　model　achieves　high　fidelity　in　predicting　life　cycle　carbon　emissions　(LCCE),　life　cycle　cost　(LCC),　and　indoor　discomfort　hours　(IDH)　through　input　feature　screening　and　hyperparameter　optimization.　Comparing　optimization　algorithms,　Two-Archive　Evolutionary　Algorithm　for　Constrained　multi-objective　optimization　(C-TAEA)　demonstrates　better　convergence　degree　and　optimization　effect.　Among　the　tested　multi-criteria　decision　making　methods,　the　VIKOR　method　selects　the　best　building　resilient　design　scheme　from　the　Pareto　data　set,　effectively　reducing　LCCE,　LCC,　and　IDH,　and　leading　to　an　overall　improvement　in　building　life　cycle　performance.　Applying　this　framework,　the　finalized　scheme　for　an　office　building　in　cold　region　optimizes　44.0%　of　LCCE,　8.2%　of　LCC,　and　4.3%　of　IDH.　This　framework　develops　a　new　approach　to　improve　building　life　cycle　performance　under　future　climate　uncertainty　and　supports　informed　decision-making　for　resilient　building　design.　To　promote　carbon　neutrality　in　construction,　it　is　urged　that　future　studies　incorporate　additional　sources　of　uncertainty,　such　as　occupant　behavior,　into　the　proposed　framework.　©　2023　Elsevier　B.V.