<p>3D printing technology is pioneering a new paradigm in smart manufacturing through its precise and controllable capability to form complex structures. However, the performance of printed products is constrained by the coupled effects of multiple factors—including model structure, slicing algorithms, and process parameters—making optimization particularly challenging. To systematically enhance the performance of printed products, this paper proposes a preliminary three-stage conceptual framework: “Structural Optimization—Slicing Strategy and Trajectory Planning—Machine Learning-Driven Full-Process Optimization.” Regarding structural optimization, this paper outlines optimization strategies for material distribution and structural morphology from three dimensions: topology optimization, lattice optimization, and biomimetic optimization. Simultaneously, the slicing algorithm system is divided into two components: slicing strategy and path planning strategy, with their respective optimization strategies discussed separately. Finally, we explored the application of machine learning algorithms throughout the entire process, including topology optimization, process optimization, and real-time optimization. Additionally, we looked ahead to future development directions such as digital twins and cross-scale coupled optimization. This will provide theoretical references and technical support for enhancing the performance optimization of 3D-printed products.</p> Graphical abstract <p></p>