Image ranking has long been studied, yet it remains a very challenging problem. Increasingly, online images come with additional metadata such as user annotations and geographic coordinates. They provide rich complementary information. We propose to combine such multimodal information through a unified hypergraph to improve image retrieval performance. Hypergraphs allow for the simultaneously capture of higher order relationships among images using different modalities, e.g. visual content, user tags, and geo-locations. Each image is represented as a vertex in the hypergraph. Each hyperedge is formed by a vertex and it's k-nearest neighbors. Three types of hyperedges exist in our unified hypergraph, which are in correspondence to the three different modalities. Image ranking is then formulated as a ranking problem on a unified hypergraph. The proposed method can easily be extended to incorporate additional modalities as long as a similarity function exists to compare the features. Experimental results on large datasets are promising.