As pivotal supporting technologies for smart manufacturing and digital engineering, model-based and data-driven methods have been widely applied in many industrial fields, such as product design, process monitoring, and smart maintenance. While promising, both methods have issues that need to be addressed. For example, model-based methods are limited by low computational accuracy and a high computational burden, and data-driven methods always suffer from poor interpretability and redundant features. To address these issues, the concept of data-model fusion (DMF) emerges as a promising solution. DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods. Despite growing efforts in the field of DMF, a unanimous definition of DMF remains elusive, and a general framework of DMF has been rarely discussed. This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods. Subsequently, this paper also presents the definition and categorization of DMF and discusses the general framework of DMF. Moreover, the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering. Finally, this paper directs the future directions of DMF.