This study focuses on evaluating the load-bearing capacity of cold-formed steel (CFS) equal lipped angle components. It delves into the structural behavior and design methods, taking into account various edge conditions and buckling modes. By considering the key parameters, which are component length, component thickness, total lip length, and lip bending angle, a total of 555 numerical models are developed for a systematic analysis on the components with two types of cross-sectional shapes. Since the coefficients within the direct strength method (DSM) inadequately capture the ultimate load-bearing capacity of lipped angle components, we introduce a design methodology rooted in the DSM framework to address global and global-local coupling buckling modes while imposing constraints to prevent local-distortion coupling buckling modes. The machine learning models (XGBoost, BPNN, and 1D-CNN) are adopted to predict the structural behavior of the two types of lipped angle components. These models can integrate the analysis of the components with two types of cross-section shapes in predictions of ultimate load capacity, axial deformation, and buckling modes. Meanwhile, the prediction accuracy of the machine learning models surpasses that of the analytical design methodology, offering more accurate prediction for ultimate load capacity, axial deformation and buckling modes.