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Abstract The hyoid bone has been identified as sexually dimorphic in various populations. The current study is a forerunner analysis that used three-dimensional multidetector computed tomography (3D MDCT) images of the hyoid bone to examine sexual dimorphism in the Egyptian population. A total of 300 subjects underwent neck CT imaging, with an additional 60 subjects randomly selected for model validation. Ten hyoid variables were measured. Initially, the dataset was subjected to discriminant analysis to predict sex and the critical variables associated with sexual dimorphism. Subsequently, machine learning approaches were employed to enhance the accuracy of sex determination. The results indicated that all measured dimensions of the hyoid bone were substantially larger in males confront to females. Discriminant functions combining four measurements (major and minor axes of the hyoid body, the distance between the lesser horns, and hyoid bone length) achieved a higher accuracy of sex prediction compared to univariate functions. The accuracies of machine learning models ranged from 0.8667 to 0.933 with precision, recall, and F1-scores also showing improvements. These findings underscore the robustness and reliability of hyoid bone in sex discrimination among Egyptians, supported by both traditional statistical methods and machine learning approaches, and could prove invaluable in forensic cases.