Local buckling of steel and excessive spalling of concrete have necessitated the need for the evaluation of reinforced concrete columns subjected to axial compression loading. Thus, this study investigates the behaviour of concrete filled steel tube (CFST) columns and reinforced concrete filled steel tube (RCFST) columns under the axial compression using the finite element modelling and machine learning (ML) techniques. To achieve this aim, a total of 85 columns from existing studies were analysed utilising the finite element modelling. The ultimate load of the generated datasets was predicted employing various ML techniques. The findings showed that the columns’ compressive strength, ductility, and toughness were improved by reducing transverse reinforcement spacing, increasing the number of reinforcing bars, and increasing the thickness and yield strength of outer steel tube. Under the axial compression loading, the finite element modelling analysis provided an accurate assessment of the structural performance of the RCFST columns. Compared to other ML approaches, gradient boosting exhibited the best performance metrics with R² and root mean square error values of 99.925% and 0.00708 and 99.863% and 0.00717 respectively in training and testing stages, to predict the columns’ ultimate load. Overall, gradient boosting can be applied in the ultimate load prediction of CFST and RCFST columns under the axial compression, conserving resources, time, and cost in the investigation of the ultimate load of columns through laboratory testing.