Blind underwater image quality assessment (BUIQA) plays a vital role in evaluating underwater image enhancement algorithms, especially in scenarios where pristine reference images are unavailable. Traditional IQA methods struggle with underwater image distortions like haze, low light, and noise. This paper proposes a novel ensemble vision transformer (VIT)-based BUIQA approach specifically designed to address these challenges. Our method leverages the strengths of ensemble VITs to capture the complexities of underwater images and predict their quality without reference. We conduct comprehensive evaluations on two publicly available underwater image datasets with ground-truth quality scores. The proposed method achieves state-of-the-art accuracy, outperforming existing methods on both datasets. These findings demonstrate the potential of our ensemble VIT approach as a valuable tool for BUIQA, with applications in tasks such as autonomous underwater vehicle navigation systems.