Reliability and performance in microelectronics and composite materials are strongly influenced by hidden defects (voids, delaminations, poor interfaces) and local variations in thermal resistance. Conventional inspection techniques often trade off speed, resolution, and interpretability. This project advances machine learning (ML) pipelines for high-fidelity defect detection and thermal mapping by fusing multi-modal datasets (e.g., thermoreflectance, infrared thermography, scanning thermal microscopy, electrical self-heating) with physics-aware feature engineering. The aim is to improve sensitivity to sub-surface anomalies and produce quantitative maps of interfacial thermal resistance, enabling earlier detection, process control, and predictive maintenance. The project will build on our existing experimental datasets and simulation “digital twin-inspired” models to benchmark new ML models and uncertainty quantification, targeting a co-authored publication.