Springback (SBP) is a critical phenomenon in metal forming processes, influencing the dimensional accuracy and mechanical integrity of manufactured components. This study investigates the springback behavior of aluminum, copper, and pure iron using a hybrid approach that integrates finite element analysis (FEA) and machine learning (ML). The research evaluates key parameters, including material deformation, peak forming force, stress distribution, and thermal effects, under varying thicknesses and punch radii. Results reveal that aluminum exhibits the highest springback (6.2%) due to its ductility, followed by copper (4.0%) and pure iron (2.5%), which demonstrated superior dimensional stability. The forming force requirements were lowest for aluminum (50 kN), moderate for copper (75 kN), and highest for iron (100 kN), reflecting their respective material strengths. Copper recorded the highest temperature rise (350°C), while iron exhibited the greatest Von Mises stress (420 MPa), emphasizing its robustness but susceptibility to localized stress. The hybrid FEA-ML model effectively predicted springback angles with high accuracy, optimizing forming parameters and minimizing experimental reliance. These findings highlight the significance of material selection and process optimization in industrial applications, where aluminum is ideal for lightweight structures, iron for strength-critical designs, and copper for high-ductility requirements. This study offers a novel framework for enhancing precision in metal forming processes, with implications for automotive, aerospace, and structural industries. Future research can extend this model to complex geometries and multi-material systems, advancing sustainable and efficient manufacturing technologies.