This study introduces a theoretical framework for the development and deployment of digital twins (DTs), with a focus on creating behavioral models using synthetic data. The aim is to enhance assembly line operations in smart manufacturing by improving process efficiency and product quality. The study details the types of synthetic data generated, the simulations conducted, and their outcomes, emphasizing significant improvements in operational processes. Key objectives include streamlining assembly processes, raising product standards, reducing machine downtime, and adjusting production to demand fluctuations. The research explores various categories of synthetic data, such as cycle times, machine performance metrics, product quality indicators, production volumes, and maintenance logs. Simulations with these datasets demonstrate the DT’s capability to predict and address production challenges effectively. The findings underscore the potential benefits of integrating DTs into manufacturing workflows, offering valuable insights for researchers and industry professionals. Additionally, the paper emphasizes the importance of validating these models with real-world data in future studies, including incorporating advanced AI features and verifying the methodology within actual manufacturing environments.