Chronic exposure to inorganic arsenic from drinking water has been associated with a host of cancer and noncancer diseases. The application of metabolomics in epidemiologic studies may allow researchers to identify biomarkers associated with arsenic exposure and its health effects. Our goal was to evaluate the long-term reproducibility of urinary metabolites and associations between reproducible metabolites and arsenic exposure. We studied samples and data from 112 nonsmoking participants (58 men and 54 women) who were free of any major chronic diseases and who were enrolled in the Health Effects of Arsenic Longitudinal Study (HEALS), a large prospective cohort study in Bangladesh. Using a global gas chromatography-mass spectrometry platform, we measured metabolites in their urine samples, which were collected at baseline and again 2 y apart, and estimated intraclass correlation coefficients (ICCs). Linear regression was used to assess the association between arsenic exposure at baseline and metabolite levels in baseline urine samples. We identified 2,519 molecular features that were present in all 224 urine samples from the 112 participants, of which 301 had an ICC of ≥0.60. Of the 301 molecular features, water arsenic was significantly related to 31 molecular features and urinary arsenic was significantly related to 74 molecular features after adjusting for multiple comparisons. Six metabolites with a confirmed identity were identified from the 82 molecular features that were significantly associated with either water arsenic or urinary arsenic after adjustment for multiple comparisons. Our study identified urinary metabolites with long-term reproducibility that were associated with arsenic exposure. The data established the feasibility of using metabolomics in future larger studies. https://doi.org/10.1289/EHP1992.