The need to have lighter and high-performance parts in industries like that of aerospace, automotive, and civil engineering has advanced the use of additive manufacturing technologies, especially through composite materials. The study examines the aspect of optimizing the 3D printed composite materials so that they can have improved mechanical behavior and maintain low weights, therefore, taking care of both the issues of structural efficiency and material sustainability. A synergistic blend of design and process parameter optimization was used in which the potential of the computational tools, finite element analysis (FEA) and machine learning algorithms were utilized. Carbon fibers reinforced polymer matrix composites have been chosen because of their good strength-to-weight ratio and compatibility with fused deposition modeling (FDM) methods. Both simulation experiments and experimental works were used to investigate the mechanical effect of critical parameters, including layer height, infill density, print orientation, and fiber placement. Findings show that optimized implementations, such as topology optimization of lattices and parameter optimization, resulted into a 0-25 % reduction in weight, without sacrificing structural integrity. The results highlight the importance of more sophisticated optimization frameworks on enhancing the scalability, and industrial feasibility of 3D printed composites. The research is of special value in the design of lightweight structural solutions as it is possible to use them in more industries where the mixture of performance and weight is paramount. Future research efforts on environmental sustainability and AI based adaptive control systems in real-time optimization could be focused on process optimization.