Traumatic brain injury (TBI) represents a significant global health challenge, affecting over 1.7 million people annually. Often referred to as concussions, mild TBIs (mTBIs) can frequently go undetected, yet they have the potential to cause brain damage. Legislation across all 50 states in the U.S. addresses sports-related mild traumatic brain injury (mTBI), requiring medical clearance before youth players can return to play. However, there currently lacks an objective, unbiased method to pre-screen potential mTBI sufferers and diagnose mTBI. While imaging holds promise as an objective diagnostic tool, it is expensive and logistically challenging. Wearable devices that monitor head impacts offer a promising pre-screening method for individuals susceptible to mTBI, while biomechanics computation can link these wearable devices to imaging and mTBI pathologies. This dissertation advances TBI biomechanics modeling by integrating machine learning techniques with large animal models, enhancing the precision and applicability of biomechanical modeling for improved TBI risk assessment. The computational biomechanics modeling of TBI typically involves the sequence of head impact, head movement kinematics, brain deformation, and resulting injuries. Traditional computational modeling methods encounter challenges such as imprecise kinematic measurements in humans, time-intensive modeling processes, limited generalizability across various types of head impacts, and missing link among biomechanics modeling, imaging and pathology. To address these limitations, my research leverages extensive simulated, real-world head impact and animal modeling data collected to optimize the accuracy, speed, generalizability, interpretability and cross-species translatabillity of the TBI biomechanics modeling process. To reduce the time consumption, machine learning head models have been developed to rapidly compute brain strain from head kinematics. To improve the accuracy, deep learning-based models have been employed to denoise kinematic measurements obtained from wearable sensors. Additionally, transfer learning and unsupervised domain adaptation techniques have been utilized to generalize the machine learning head models to diverse types of head impacts. Furthermore, to bridge the gap between biomechanics and medical imaging for enhanced mild TBI diagnosis, a novel impact porcine model has been devised to establish connections between biomechanics, neuroimaging, and histopathology