The mechanical behavior of 3D-printed composite materials continues to raise interest due to the complex anisotropy introduced during the manufacturing process. While several studies have addressed the performance of these materials, many assume isotropic behavior, failing to capture the influence of printing direction and interlayer effects. In addition, numerical models often focus on an isotropic damage onset without fully accounting for nonlinear anisotropic responses. To address this challenge, this work presents a methodology combining Space Mapping theory with the Serial-Parallel Mixing theory to model the mechanical response of additively manufactured materials with enhanced accuracy. The proposed approach begins by using Space Mapping theory to transform the inherently anisotropic behavior of the printed material into a simplified isotropic problem, enabling the application of well-established nonlinear isotropic formulations, while preserving the directional nature of the material behavior. This approach improves simulation accuracy and supports the optimization of printed components in engineering applications. Building on this foundation, we introduce continuous carbon fiber layers, incorporating the Serial-Parallel Mixing theory to model the hybrid structure formed by the combination of printed matrix and a continuous fiber reinforcement. This layered structure results in a composite with superior mechanical properties and directional reinforcement. To demonstrate the effectiveness of the proposed methodology, a case study involving a printed structural panel adding an unidirectional continuous fiber layer reinforcement is presented. The panel, designed for enhanced stiffness and strength, was subjected to mechanical testing under cyclic loading conditions. The simulation, based on the Space Mapping and Serial-Parallel Mixing formulations, accurately replicated the observed mechanical behavior, validating the approach and demonstrating its potential for optimizing composite structures produced via additive manufacturing.