Image data has grown rapidly because of advances on photo capturing devices. In traditional, because the image data has not been huge, most past studies focused on the effectiveness improvement. However, accessing the images from a huge amount of image data needs a large cost. Hence, how to perform efficient image retrieval has been a hot topic in the last few decades. To this end, in this paper, we propose efficient big image data retrieval by using clustering index and parallel computation. In the offline stage, the images are grouped into a number of clusters. In the online stage, the relevant images to the query image are retrieved by a level-wise search. Our intent is to conduct a more efficient image retrieval method in comparison with traditional methods but keep the same effectiveness still. In the experiments, four types of retrieval are compared and our proposed parallelized image data retrieval is much faster than the other compared methods under the very close accuracies.