The development of resilient and adaptive manufacturing systems requires efficient methods for predicting the mechanical properties of additively manufactured (AM) components, particularly as these systems increasingly leverage digital technologies for rapid reconfiguration. This study presents a hybrid approach that integrates the theoretical method with finite element analysis (FEA) to accurately predict the tensile and torsional properties of AM parts while significantly reducing computational time. Two FEA models were evaluated: a standard model with detailed material and geometric representations, and a simplified model optimised for computational efficiency. The proposed methodology utilises G-code data from AM processes to generate a replicated CAD model, streamlining the analysis while maintaining predictive accuracy. The findings demonstrate that this approach can quickly establish manufacturing resource capabilities, thereby supporting the agile reconfiguration of production systems. By reducing reliance on extensive physical testing, the hybrid FEA-theoretical approach enhances sustainability through material and energy savings, contributing to the development of smart, resilient, and sustainable manufacturing systems aligned with the principles of Industry 4.0 and beyond.