The advent of Industry 4.0 has transformed the manufacturing landscape, enabling unprecedented levels of automation and data-driven decision-making. Central to this transformation are Digital Twins (DT) and Graph Neural Networks (GNNs), which together offer innovative solutions for optimizing manufacturing processes. This paper delves into the integration of GNNs with digital twins and establishes their relevance in tapping into real-time data, whereby industries can enhance processes such as maintenance forecasting, improve supply chain efficiency, assure quality management, and promote sustainable complex relational modeling. GNNs also enable better insights and more explainability in manufacturing systems. Yet, challenges exist, such as data quality and scalability. Unlocking these advancements hinges on regulatory compliance. The conclusion ends with a thorough discussion of future directions, highlighting significant aspects such as data management, model interpretability, and training the workforce toward fruitful benefits in GNNs and the implementation of digital twin technology in the manufacturing industry.