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Estimating the axial strength of steel tube confined concrete (STCC) columns is challenging because it depends nonlinearly on the concrete compressive strength, the yield stress of steel, the column diameter (D), the thickness of steel tube (t), column length (L), D/t, and L/D. This study proposed an optimized hybrid machine learning(ML) model for accurately predicting the axial strength in STCC columns, which integrated support vector regression (SVR) and grey wolf optimization algorithm (GWO). Artificial neural networks (ANNs), SVR, linear regression, random forests (RF), and M5P rule were applied as baseline models. 136 samples of STCC columns infilled with various strength concrete were collected to develop and evaluate the proposed model. The results revealed that the proposed model was the most powerful compared to baseline models. Predicted data produced by the proposed model show the …