Ankle foot orthoses (AFOs) are commonly recommended for individuals with cerebral palsy (CP) as a means to improve gait. Goals of this dissertation were to evaluate the current efficacy of AFO use for children with CP, investigate the biomechanical mechanism of how AFOs influence gait, and describe new methods for analyzing and improving AFO outcomes as they pertain to gait. Retrospective data analysis, statistical machine learning, and simulation techniques were used to achieve these goals. Data analysis revealed that the general efficacy of AFO use was poor. However, a data driven model developed through machine learning techniques suggests that efficacy can likely be improved by using the model to recommend AFO prescriptions for individuals that are predicted to improve their gait with AFO use and refrain from prescribing AFOs for individuals whose gait will not improve with AFO use. Investigations of gait efficiency and muscle function revealed new factors that could potentially be leveraged to improve the efficacy of AFO use. Finally, an AFO design redundancy between two commonly prescribed AFOs was identified, eliminating misconceptions about the efficacy of a redundant AFO design. The techniques and conclusions presented in this dissertation have the potential to significantly improve the efficacy of AFO use for children with CP.