This　study　presents　a　novel　probabilistic　machine　learning　(ML)　approach　using　Natural　Gradient　Boosting　(NGBoost)　to　predict　the　axial　compressive　capacity　of　Concrete　Filled　Steel　Tube　(CFST)　columns.　Leveraging　a　comprehensive　dataset　of　1,127　experimentally　tested　CFST　specimens　under　axial　compressive　loads,　we　compare　the　performance　of　various　ML　algorithms.　These　include　deterministic　models　like　eXtreme　Gradient　Boosting　(XGBoost)　and　Artificial　Neural　Networks　(ANN),　and　probabilistic　models　such　as　XGBoostDistribution　(XGBD)　and　NGBoost.　The　NGBoost　model,　which　employs　Normal　and　LogNormal　distributions　to　account　for　uncertainties　in　input　data,　demonstrates　superior　predictive　accuracy　and　robustness.　SHapley　Additive　exPlanations　(SHAP)　are　utilized　to　interpret　the　influence　of　input　features,　providing　insights　into　the　relative　importance　of　different　structural　parameters.　The　predictive　performance　of　the　NGBoost　model　with　LogNormal　distribution　is　benchmarked　against　existing　design　codes,　including　Eurocode　4,　ANSI/AISC　360-22　AS/NZS　2327,　and　Chinese　Standard　(GB50936-2014),　showcasing　its　enhanced　accuracy　and　reliability.　This　approach　not　only　improves　predictive　performances　but　also　integrates　uncertainty　quantification,　making　it　highly　suitable　for　design　applications　in　Civil　Engineering　where　understanding　the　variability　in　the　structural　behavior　is　crucial.