Objective: Machine learning can automatically extract valuable insights from vast datasets, predict and classify diseases, and evaluate drug efficacy. To assess the effectiveness of machine learning algorithms in analyzing non-formed components in urine, real medical data were processed and annotated. Methods: Five models, including K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Machines, and Gaussian distributions,were constructed to quantitatively analyze 12 non-formed urine components, such as vitamin C, white blood cells, and urinary bilirubin. The efficacy of these models was then compared. Results: It was found that the RandomForest model outperformed others, achieving the lowest mean squared error, high recall rate, accuracy, and areaunder the curve. Conclusions: These findings indicate that machine learning offers significant potential for studying non-formed urine components, potentially enhancing the precision and effectiveness of disease detection andproviding valuable support for clinical decision-making.