3D graphical data, commonly represented using triangular meshes, are deployed in a wide range of application processes including compression, filtering, watermarking, and simplification. These processes often introduce geometric distortions which affect the visual quality of the ultimate data visualization. In order to accurately evaluate perceptual impacts caused by the distortions, assessment metrics on 3D Mesh Visual Quality (MVQ) have been extensively discussed in the literature. Researchers recommended various metrics to predict the adverse effects that visual artifacts can have in applications. Most of these metrics are based on geometric attributes, \eg conventional geometric distance, Laplacian coordinates, different types of curvature computation, and dihedral angles. We hypothesize that an optimal combination of multiple attributes associated with a 3D mesh surface can contribute to better perceptual prediction than single attributes used separately. In this paper, we use two user studies to validate our hypothesis. Our contributions are: (1) providing a detailed analysis of the most relevant geometric attributes for mesh quality assessment, and (2) introducing a new perceptual evaluation metric based on multiple attributes, with the optimal combination determined through machine learning techniques. Statistical quantitative analysis shows that our metric delivers better results than other state-of-the-art approaches. The proposed method is simple to implement and fast in execution. Moreover, our framework can easily be expanded to accommodate additional surface attributes.