The digital transformation has fundamentally reshaped manufacturing, driving the evolution of smart manufacturing systems that leverage data for enhanced efficiency and innovative solutions. However, the practical implementation of smart manufacturing faces significant challenges, with big data emerging as a critical bottleneck. Effectively managing and analyzing large datasets requires identifying relevant process parameters and ensuring data quality, as issues such as incompleteness, imbalance, and fuzziness can undermine decision-making and predictive analytics. To address these challenges, synthetic data has gained attention as a powerful tool in smart manufacturing. By replicating real-world scenarios and enabling greater control over data variability, synthetic data facilitates the training of artificial intelligence (AI) models for applications such as predictive maintenance, anomaly detection, and quality control. It also allows for the simulation of rare events and mitigates privacy concerns. Despite these advantages, synthetic data faces limitations, including potential fidelity issues and dependency on the quality of the original data. This research explores the opportunities and challenges of synthetic data in smart manufacturing through a systematic literature review, offering insights into its generation, application, and potential to accelerate the deployment of smart manufacturing systems.