Human language learning differs significantly across individuals in the process and ultimate attainment. Although decades of research exploring the neural substrates of language learning have identified distinct and overlapping brain networks subserving learning of different components, the neural mechanisms that drive the large interindividual differences in learning success are still far from being understood. In this talk, I will introduce recent works from my group on neural mechanisms of language learning across multiple components (e.g., auditory and speech category learning, word and grammar learning). With machine learning and predictive modeling techniques, we have identified potential neuromarkers that showed robust predictive powers of individual learning success. Identifying the neuromarkers has direct implications for constructing contemporary cognitive neuroscience models of language learning and informing educational practice and clinical research. The neuromarkers and mechanisms derived from this line of work have the potential to be applied to predicting individual language improvement and outcomes for populations with language deficits and developmental delays. The neuromarkers and prediction techniques could be leveraged in designing individualized training and intervention paradigms that maximize learning efficacy and minimize individual differences in outcome.