The performance in the 110-m hurdles at the sprint hurdles event is determined by several physical and physiological qualities. Nonetheless, relatively little attention has been paid to the predictability of such factors in determining race performance. This study seeks to fill this gap by establishing the most critical physical and physiological characteristics affecting elite hurdlers’ performance and creating a statistical model that predicts race times from the identified measurable characteristics. The study utilized a descriptive research design involving six elite male hurdlers, all of whom completed a battery of standardized physical and functional tests to assess their explosive lower-body strength, agility, reaction time, and anaerobic capacity. Vertical jump height, zigzag agility test results, reaction time, and shuttle run endurance were examined using validated sports performance assessment protocols. a multiple linear regression analysis was conducted to construct a model using all these attributes of 110-m hurdle race times. It was found that 97% of the difference (R2 = 0.970) in performance over the hurdles could be explained by four variables: vertical leap (explosive power), zigzag agility (change-of-direction speed), reaction time and anaerobic endurance; this was one of the most predictive models generated for this event. The results of this study can serve as recommendation for integrated sprint hurdle training covering a wide range of aspects, explosive strength, agility and neuromuscular response times related to sprint hurdle performance. In any case, the results highlight the prioritization of anaerobic stamina to ensure the maintenance of high intensity over time in the race. Further study is needed with larger and more diverse athlete populations to improve model power. In addition, machine learning models using artificial neural networks can improve the accuracy of the model by revealing associations, non-linear relationships among biomechanics, physiology and psychology. Technologies in motion-capture systems, muscle activation study and psychological profiling would change the way we evaluate athletes and continuously push the frontiers of sports performance science.