Aim: The main objective of this study was to predict upper urinary tract damage utilizing novel approaches, such as machine learning models, by incorporating simple predictors alongside established radiological and clinical factors. Materials and Methods: In this retrospective study, a total of 191 patients who underwent blood tests, urine analysis, imaging, and urodynamic studies (UDS) in order to assess their nephrological and urological status were included. Basic statistical analyses were conducted using IBM SPSS Version 25. A significance level of p