A dipstick urinalysis test is performed by immersing a reagent strip in the urine specimen and then comparing the resulting reagent pad colors with a reference key. The color assessment of the reagent strip can be performed manually or by using a urine analyzer. However, the manual procedure is prone to subjective inaccuracies in varying ambient illumination and urine analyzer equipment is expensive. This paper presents a smartphone-based machine-learning approach to accurately determine the reagent pad colors for automated assessment. We start with a unique calibration chart and use multivariate linear regression to map the captured color values to their true equivalents. This accounts for the camera-induced distortions and ambient illumination factors. Subsequently, the color comparison is performed using the least Euclidean distance to match the calibrated color of each reagent pad with the reference key. The results from an experimental study, using five different smartphone cameras and three common illumination settings, indicate a high degree of accuracy in color assessment for synthetic dipsticks. The proposed smartphone-based method is an easy-to-perform, time-efficient, and cost-effective solution for an automated urinalysis and could be used as an alternative to manual reading or benchtop urine analyzers. Clinical Relevance- The methods, technology, and data reported in this research can serve as an accurate, reliable, and cost-effective means for automated urinalysis in comparison to the existing methods. Furthermore, the ubiquity of smartphones opens new avenues for automated diagnostics in clinical, at-home, and point-of-care settings.