Rolling contact fatigue (RCF)-induced cracks in steel rails exhibit a fish-scale-shaped cluster distribution, and generally form in a layered, overlapping manner. Eddy-current-pulsed thermography (ECPT) has been applied in RCF detection by taking advantage of electromagnetic–thermal execution; however, one still faces challenges in identifying and quantifying such layered, overlapping defects. This paper proposes a swept-frequency eddy-current-pulsed thermal tomography (ECPTT) detection method to quantitatively characterize multilayer crack depth and inclination angle in an artificial rail sample. In particular, stimulating frequency modulation is used to guide the induced eddy current and heat to varying depths, and this is combined with principal component analysis (PCA) to identify multilayer defects. Moreover, a thermal signal reconstruction (TSR) algorithm is introduced. TSR features are extracted for analyzing the burial depth and inclination angle of multilayer defects. The results demonstrate that the third principal component (PC3), extracted via PCA, enables layer-count discrimination in multilayer defects. Integrated with gradient magnitude analysis of the second principal component (PC2) under swept-frequency excitation, defect contour localization error can be controlled within 0.5 mm. Building on layer discrimination, multi-frequency thermal response analysis further reveals variations in PC1’s variance contribution, differentiating inclination angles of 10° and 20°, whereas comparative heating- and cooling-rate magnitudes distinguish burial depths of 0.5 mm and 1.0 mm. The research verifies that the ECPTT system can accurately detect the layer number, inclination angle, and depth of buried RCF defects, substantially enhancing the accuracy of defect contour reconstruction.