In structural stability analyses, determining the critical buckling load is a crucial issue. Regression, fuzzy logic and Artificial Neural Network (ANN) algorithms can be used to determine critical buckling loads. This study compares the results of different approaches for column buckling load prediction. Regression, Fuzzy logic and ANN algorithms were employed in the analyses, representing material properties to take uncertainties into account and the results were compared. The results show that uncertainties play an important role in stability analyses and should be considered in the design. The elastic modulus results predicted by regression, fuzzy logic and ANN are also compared to those obtained using empirical results of the buildings codes and various models. These comparisons show that obtained results in the present study give closer results than the different design codes. Therefore, proposed models can be used for critical buckling loads and regression, fuzzy logic and ANN have strong potential as a feasible tool for estimating column buckling loads within the range of input parameters considered.