Machine learning (ML) techniques have been recently employed to facilitate the development of novel two-dimensional (2D) materials. Among various synthesis approaches, chemical vapor deposition (CVD) has demonstrated tremendous potential in producing high-quality 2D flakes with good controllability, enabling large-scale production at a relatively low cost. Traditionally, the quality of CVD-grown samples can be manually evaluated based on optical images which is labor-intensive and time-consuming. In this paper, we explored a data-driven unsupervised quality assessment strategy based on image clustering via integrating self-organizing map (SOM) and k-means methods for optical image analysis of CVD-grown 2D materials. The high matching rate between the clustering results and material experts' labels indicated a good accuracy of the proposed clustering algorithm. The proposed unsupervised ML methodology will provide materials scientists with an effective tool kit for efficient evaluation of CVD-grown materials' quality and has a broad applicability for various material systems.