The distribution characteristics of internal distress in pavement structures and the corresponding maintenance decisions are crucial for improving road performance and extending service life. Based on three-dimensional ground-penetrating radar (3D GPR) technology, this study investigated the internal distress of a 12-kilometer section of both driving and overtaking lanes of an expressway in Hubei Province. High-resolution images of internal pavement distress were obtained, and milling maintenance decisions were optimized by employing these images and a modular decision-making model. Through an efficient and non-destructive technique, the distribution characteristics of internal pavement distress were analyzed, and potential distress types were identified, providing a scientific basis for maintenance decision-making. Data analysis revealed the crack rates, damage rates, and distribution characteristics of distresses between different layers and quantified the severity of the damages. Based on the distribution characteristics of distresses, a modular and layered maintenance decision-making method was proposed, and milling treatment plans were developed in response to the crack and damage rates. The study demonstrates that 3D GPR can efficiently and non-destructively identify and assess internal pavement distress and provide accurate data for maintenance decision-making and resource optimization, thus significantly enhancing maintenance efficiency and economic benefits.