High-power laser beam welding (LBW) is a widely used joining technique for metallic materials. However, porosity defects frequently arise during the process, leading to significant degradation of weld properties. Accurate prediction of porosity occurrence remains challenging due to the highly nonlinear, material-dependent underlying physics involved. In this study, a universal physics-informed machine learning framework is proposed to predict porosity levels in the LBW of aluminum and steel alloys. Systematic LBW experiments are conducted to quantify porosity ratios across a broad parametric space. In parallel, a well-validated multiphysics simulation model is employed to characterize weld pool behavior and keyhole dynamics under corresponding conditions. By selecting relevant physical variables and incorporating them into dimensionless features with explicit physical significance, capturing keyhole stability, bubble dynamics, and bubble entrapment, the proposed model demonstrates strong predictive performance of porosity ratio across different metallic systems. A general root mean square error of 1.47 is achieved. Furthermore, the potential universal mechanisms governing porosity formation for different metallic materials are identified, analyzed, and, for the first time, hierarchically evaluated. It is found that the Stokes number along the welding direction, the weld pool ratio, and the keyhole ratio are the three most dominant physical factors governing porosity formation.