Traditional inspection methods for wind turbines and other energy infrastructure are limited by accessibility, accuracy, and efficiency. Detecting subsurface defects, such as delamination, moisture infiltration, or early-stage cracking, is especially challenging using standard visual inspections or thermal imaging, as these methods often fail to penetrate surface coatings or composite materials. Short-Wave Infrared (SWIR) imaging, deployed via autonomous drones, offers a non-invasive, high-precision solution. This drone-mounted system, combined with deep learning, would enable energy operators to rapidly identify and assess structural weaknesses before they escalate into costly failures. By utilizing high-resolution SWIR cameras, our drone-based inspection system can capture detailed imagery beyond the visible spectrum, revealing hidden cracks, corrosion, and material degradation that conventional inspections overlook. The ability to scan extensive infrastructure assets in a fraction of the time—without requiring scaffolding, rope access, or prolonged shutdowns—reduces operational disruptions while significantly improving safety and cost efficiency. More than just data collection, this system integrates AI-driven defect classification, allowing for automated anomaly detection and predictive assessments tailored to each asset’s operational conditions. When combined with PINN-based predictive analytics, SWIR drone inspections create a multi-layered, intelligent monitoring system that enhances long-term reliability, minimizes maintenance costs, and supports proactive decision-making. By replacing reactive, labor-intensive inspections with fast, automated, and data-driven assessments, energy operators gain greater visibility into the health of critical infrastructure, ensuring optimal performance and extended service life across wind farms, power plants, and industrial foundations.