Background: Urine tests such as urine cytology are commonly used for the diagnosis and monitoring of urothelial cancer. These tests are often limited by issues related to sensitivity or specificity. It is well known that derangement of cellular metabolism is one of the hallmarks of carcinogenesis. As urothelial cancer is in constant contact with urine, we hypothesize that metabolite composition in the urine may provide insight into possible urothelial cancer presence in the urinary tract. In this study, we evaluated a metabolomics based urine test for the detection of urothelial cancer. Methods: In this prospective, multi-institutional IRB approved study, urine samples were collected from a total of 57 urothelial cancer patients and non-urothelial cancer controls. Gas chromatography profiles of urine small molecule metabolites were generated to yield over 2400 data points of metabolite peaks and troughs for every urine sample. A machine-learning based algorithm (Abilis Life Sciences) was constructed to predict urothelial cancer versus non-cancer controls through analysis of peaks and trough patterns of urine metabolomics profiles. Predictions were made in a blinded fashion and descriptive statistics of test sensitivity and specificity were generated. Results: The urine metabolite composition of 57 patients were analyzed and urothelial cancer predictions were generated. The test demonstrated an overall accuracy of 89.5% (51 out of 57 cases correctly predicted). The sensitivity of the test was 97.1% (34 out of 35) and specificity was 77.3% (17 out of 22). The Positive Predictive Value is 87.2%, while the Negative Predictive Value is 94.4%. The area under the curve for the receiver operating characteristic curve was 0.87. Conclusions: Urine based metabolic profile analysis using artificial intelligence algorithms is a promising potential diagnostic test for detection of urothelial cancer. Further testing is ongoing to increase robustness of the validation.