Cold-Formed Steel (CFS) is considered as a sustainable material and CFS members are becoming popular nowadays as primary or secondary load carrying structural elements. The increasing use of CFS members is owing to their other advantages like high strength to weight ratio, versatility in cross sectional shapes and dimensions, easy fabrication and mass production, uniform quality, good energy efficiency, easiness to handle and transport etc. The behaviour of cold-formed members under flexure is complex as it involves the occurrence of various buckling modes (Local, distortional or lateral-torsional buckling) and the possible interaction between them. Although, the moment carrying capacity of CFS beams can be estimated using existing codes and design standards, the present study is an attempt to introduce an alternate rapid capacity assessment tool based on machine learning for the same. Available data on experiments conducted on CFS lipped channel beams are collected to model and analyse in ABAQUS for validating the finite element model. The validated FE model is used for developing the input-output data set for training the Machine Learning (ML) models by sampling the geometric and strength related input parameters of the CFS beam. Recently introduced machine learning regression models like Linear Regression, Lasso regression, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Gradient Boosting Regressor, Support Vector Machine and Artificial Neural Network are used to predict the ultimate flexural capacity of CFS beams. The CatBoost model is emerged as the best performing algorithm for predicting the flexural capacity, with 98.5% accuracy for the test data set. A SHAP (SHapley Additive exPlanations) analysis is also conducted for the global and local interpretation of the prediction of the best performing machine learning model. SHAP results not only indicate the influence of input parameters on output prediction, but also the influence of input parameters on each other in predicting the output.