Individualized white-matter functional networks provide a new paradigm for parsing individual differences and neural mechanisms in attention-deficit/hyperactivity disorder (ADHD) among children and adolescents. This review summarizes construction approaches for such networks, including clustering analysis and independent component analysis, and synthesizes current progress. Group-level studies in children with ADHD have shown increased white-matter functional connectivity in the default mode and sensorimotor networks, but decreased white-matter functional connectivity in attention-related networks; these connectivity abnormalities are associated with core symptoms and impairments in executive function. However, group analyses can obscure inter-individual network heterogeneity and overlook gray-white matter functional coupling, limiting clinical translation. Individualized white-matter functional networks hold promise as potential neuroimaging markers for early screening and precision intervention in ADHD, yet challenges persist, including methodological inconsistency and the difficulty of acquiring pediatric data.