The rapid deterioration of road infrastructure due to aging, environmental stressors, and increasing traffic loads has heightened the need for non-destructive and accurate pavement health monitoring techniques. Ground Penetrating Radar (GPR) has emerged as a reliable tool for evaluating subsurface pavement conditions by providing high-resolution data on layer thickness, moisture content, and potential structural defects. This study presents an integrated approach to pavement health monitoring by coupling GPR data acquisition with advanced signal processing algorithms for noise reduction, feature extraction, and automated defect classification. Field measurements were conducted across urban and highway pavements to capture GPR profiles under varying surface and subsurface conditions. The raw radargrams were processed using wavelet decomposition, Hilbert–Huang transforms, and machine learning-based pattern recognition to identify anomalies such as delamination, voids, and excessive moisture. Validation was performed through core sampling and visual inspection, revealing a detection accuracy exceeding 92% for major defects. The findings underscore the potential of combining GPR with robust computational analysis for predictive pavement maintenance, cost optimization, and enhanced road safety. This research contributes to the development of automated, real-time pavement condition assessment systems suitable for large-scale infrastructure monitoring.