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The high-speed railway subgrade compaction quality is controlled by the compaction degree (<i>K</i>), with the maximum dry density (<i>ρ_dmax</i>) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the <i>ρ_d</i>_max still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the <i>ρ_d</i>_max based on the dynamic stiffness <i>K_rb</i> turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting <i>ρ_d</i>_max is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR)<i>,</i> and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in <i>ρ_d</i>_max prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of <i>ρ_d</i>_max across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (<i>H</i>₀), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting <i>ρ_d</i>_max. (2) The Bootstrap-POA-ANN interval prediction model for <i>ρ_d</i>_max can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for <i>K</i>. Comparing the <i>H</i>₀ of 50~60 cm and 60~70 cm, the compaction quality is better with the <i>H</i>₀ of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.