The springback phenomenon (SBP) is a prevalent, costly, and challenging problem. It occurs in metals during sheet metal forming processes (SMFPs). Experimental studies can have errors that prevent the target data from being acquired. Accordingly, this research aims to bridge this gap by choosing other inspection approaches, reflected in finite element analysis (FEA) and machine learning (ML) integration, to forecast probable issues of SBP in heavily utilized metals across diverse manufacturing domains, namely 99% pure aluminum, 99% pure copper, and low-carbon steel. Material deformation, peak forming force, stress distribution, and thermal effects are examined under different thicknesses and punch radii. ANSYS simulation results show that 99% pure aluminum has the highest springback (6.2%) due to its ductility, followed by 99% pure copper (4.0%) and low-carbon steel (2.5%), which has superior dimensional stability. The forming force requirements were lowest for 99% pure aluminum (50 kN), moderate for 99% pure copper (75kN), and highest for low-carbon steel (100kN). 99% pure copper had the highest temperature rise (350°C), while low-carbon steel had the highest Von Mises stress (420 MPa), demonstrating its strength but vulnerability to localized stress. The hybrid FEA-ML model has effectively and accurately predicted springback angles. The results also show that 99% pure aluminium is best for lightweight structures, low-carbon steel for strength-critical designs, and 99% pure copper for high-ductility needs.