This paper explores the integration of 3D printing and machine learning technologies in the development of smart structures, which help these structures in achieving enhanced optimization, adaptability, and intelligence, resulting in substantial improvements in sustainability, performance, and quality across diverse applications. The use of 3D printing allows for the creation of complex geometries and customized components, while machine learning algorithms enable real-time data analysis and decision-making, thus enhanced structural performance. This synergy fosters the development of structures that can monitor their own health, adapt to changing conditions, and optimize their resource use. The paper discusses various case studies and applications where the integration of these technologies has been successfully implemented and demonstrates their feasibility and effectiveness. This paper summarizes the key steps for such integration and helps streamline its processes. It serves as a practical guide for implementing these technologies. This work informs researchers, engineers, and industry professionals about the benefits and challenges of using 3D printing and machine learning in smart structures, which highlights advancements and future prospects to support the development of more resilient and efficient built environments.