Intelligence differences in children may stem from how precisely their brain networks balance excitation and inhibition—a fundamental principle that governs optimal neural computation. This finding challenges traditional views that focus on brain size or connectivity patterns as primary drivers of cognitive ability. The research examined electrical brain activity in 128 children aged 6-19, measuring how closely different brain regions operated at the theoretical 'critical point' where neural networks process information most efficiently. Higher-IQ children showed excitation-to-inhibition ratios significantly closer to the optimal value of 1.0 specifically in association cortices—brain regions responsible for complex reasoning, planning, and abstract thinking. This critical balance was notably absent in sensorimotor areas, suggesting intelligence emerges from specialized optimization in higher-order networks rather than global brain efficiency. The study also revealed that background neural noise patterns, measured through aperiodic brain activity, correlated with intelligence along a hierarchical gradient from basic sensory regions to sophisticated association areas. These neural signatures became more pronounced with age, indicating that brain criticality refinement may be a key mechanism of cognitive development. The findings represent a significant advance in understanding intelligence at the cellular level, moving beyond structural brain measures to dynamic network properties. However, the cross-sectional design limits causal interpretations, and the relationship between laboratory-measured criticality and real-world cognitive performance requires further validation across diverse populations.