The estimation of the ultimate capacity of rectangular or circular shaped steel tubular members filled with concrete, such as columns, beams, and beam-column connections, requires a detailed structural study to be carried out. Therefore, identify the concrete strength the member subjected to axial-load only. Using the Levenberg-Marquardt artificial neural network, this paper investigates the concrete-filled steel tubular (CFT) members axial strength. 201 experimental specimens were collected from the literature to obtain the best results, and a wide range of geometric and material properties of CFT members were included. The proposed design and specimens illustrate the practicality and effectiveness of the chosen CFT column approach to classify real structural results.