The performance in the 110-meter 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 in-volving 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 per-formance assessment protocols. A multiple linear regression analysis was then performed to create a model for 110-meter hurdle race times based on these attributes. The findings demonstrated that 97% of the variation (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 made it one of the most predictive models created for this event. the findings of this study can be an endorsement for integrated sprint hurdle training spanning the broad spectrum of qualities — explosive strength, agility, and neuromuscular re-sponse times — that affect sprint hurdles performance. Anyway, the results highlight the prioritization of an-aerobic stamina to ensure the maintenance of high intensity over time in the race. Future research should in-clude larger and more diverse athlete populations to enhance model strength. Furthermore, the implementation of machine learning methods such as artificial neural networks could enhance the accuracy of the model by identifying non-linear relationships among biomechanical, physiological and psychological variables. The ad-vancements in motion-capture systems, muscle activation analysis, and psychological profiling would drastical-ly increase how we assess athletes and continuously push the frontiers of sports performance science.