G. R. Iyappan, P. Sangeetha
ABSTRACT Modern structural applications are increasingly using cold‐formed steel (CFS) sections because of their high strength‐to‐weight ratio and ease of fabrication. These sections come in open (C, Z, L), closed (tubular, box), and built‐up configurations. To forecast their performance under axial, flexural, and combined loading, traditional analytical and finite element methods (FEM) are computationally demanding and frequently unsuccessful in capturing intricate buckling interactions, residual stresses, and geometric imperfections. A promising substitute is offered by recent developments in machine learning (ML), which allow for quick, highly accurate predictions based on data. According to studies, ML models like artificial neural networks (ANNs), support vector machines (SVM), convolutional neural networks (CNN), and gradient boosting can greatly increase predictive accuracy. For example, when compared to traditional methods, R 2 scores for axial strength predictions increased from 0.75 to 0.94. Three areas of future research are highlighted in this review, which critically analyzes ML applications in CFS research: (i) creating real‐time surrogate models that are integrated into design platforms; (ii) combining mechanics‐based techniques such as the direct strength method (DSM) with explainable machine learning (XML); and (iii) adding uncertainty quantification (UQ) to improve reliability. Centralized dataset creation, adaptive health monitoring, and the integration of sustainability objectives are further directions. This study offers researchers and practitioners a path forward for ML‐enabled CFS design by bridging the gap between high‐fidelity simulations and realistic design workflows. The results show that in order to guarantee safe, effective, and sustainable structures, interpretability, code compatibility, and industry adoption are as important as the transformative potential of ML.