Background: The known primary radiological diagnosis of Chiari Malformation-I (CM-I) is based on the degree of tonsillar herniation ( TH) below the Foramen Magnum (FM). However, recent data also shows the association of such malformation with smaller posterior cranial fossa (PCF) volume and the anatomical issues regarding the Odontoid. This study presents the achieved result regarding some detected potential radiological findings that may aid CM-I diagnosis using several machine learning (ML) algorithms. Materials and Methods: Between 2011 and 2020, radiological examinations of 100 clinically/radiologically proved symptomatic CM-I cases and 100 control were evaluated by matching age and gender. A team of Neuroradiologists had reviewed the MR images of the study population. A total of 11 different radiological parameters were assessed for CM-I diagnosis. The parameters were defined and examined in 5 designed different ML algorithms. Statistical analysis was conducted for data analysis. Results: The mean age of patients was 29.92 ± 15.03 years. The primary presenting symptoms were headaches (62%). Syringomyelia and retrocurved-odontoid were detected in 34% and 8% of patients, respectively. All of the morphometric measures were significantly different between the groups, except for the distance from the dens axis to the posterior margin of FM. The Radom Forest model is found to have the best 1.0 (14 of 14) ratio of accuracy in regard to 14 different combinations of morphometric features. Conclusion: This study indicates the potential usefulness of ML-guided PCF measurements, other than TH, that may be used to predict and diagnose CM-I accurately. Our results support the view of TH as a single radiological parameter may fail during the diagnosis of CM-I. Combining two or three preferable osseous structure-based parameters may increase the accuracy of radiological diagnosis of CM-I.