A systematic approach for the fusion of associated ions from a common molecule was developed to generate "one feature for one peak" metabolomics data. This approach guarantees that each molecule is equally selected as a potential biomarker and may largely enhance the chance to obtain reliable findings without employing redundant ion information. The ion fusion is based on low mass variation in contrast to the theoretical calculation measured by a high-resolution mass spectrometer, such as LTQ orbitrap, and a high correlation of ion pairs from the same molecule. The mass characteristics of isotopic distribution, neutral loss, and adduct ions were simultaneously applied to inspect each extracted ion in the range of a predefined retention time window. The correlation coefficient was computed with the corresponding intensities of each ion pair among all experimental samples. Serum metabolomics data for the investigation of hepatocellular carcinoma (HCC) and healthy controls were utilized as an example to demonstrate this strategy. In total, 609 and 1084 ion pairs were respectively found meeting one or more criteria for fusion, and therefore fused to 106 and 169 metabolite features of the datasets in the positive and negative modes, respectively. The important metabolite features were separately discovered and compared to distinguish the HCC from the healthy controls using the two datasets with and without ion fusion. The results show that the developed method can be an effective tool to process high-resolution mass spectrometry data in "omics" studies.