This study focuses on the integration of digital twins and smart sensors to optimize the direct energy deposition (DED) process for manufacturing automotive components using EN36C steel. DED, an additive manufacturing technique, enables the precise deposition of material using a concentrated energy source, such as a laser, to create complex and customized parts. EN36C steel, known for its machinability and weldability, is employed in this research for the production of high-performance automotive components. The process parameters, including laser power, scanning speed, cooling time, and stacking thickness, are monitored and optimized using smart sensors to enhance the overall process. Digital twins are developed to simulate the DED process, enabling real-time adjustments and predictive analysis of the system’s behavior under varying conditions. Microstructural analysis is conducted on the produced components to assess mechanical properties such as tensile strength and microhardness. Furthermore, temperature distribution during the DED process is measured using thermocouples and validated through a finite element model developed in Ansys. The integration of digital twins and smart sensors provides a powerful framework for optimizing the DED process, improving efficiency, and ensuring high-quality production of automotive components in Industry 5.0.