To meet the client’s need for scalable and automated tower inspection, we designed a fully integrated platform that combined drone imagery, LiDAR scans, and AI powered image recognition. Our approach focused on replacing manual review processes with intelligent automation while maintaining human oversight for quality control. The system begins with data ingestion, pulling in RGB images, LiDAR point clouds, and GPS data captured via autonomous drone missions. This data is then processed through an AI pipeline built on a combination of computer vision models trained to identify structural elements and detect defects such as missing bolts, corroded joints, or insulator cracks. To improve model accuracy, we annotated thousands of labeled tower components and failure cases. We used transfer learning with a Detectron2 backbone to speed up training and improve reliability on sparse datasets. The model runs inference on the server side, with results visualized in the dashboard as overlaid bounding boxes, semantic labels, and 3D map references. The user facing platform, built in React, offers an intuitive side by side view: high resolution images, thermal and point cloud data on one screen, and GIS integrated mapping on the other. Engineers can toggle layer visibility, validate AI findings, schedule inspections, and auto generate reports. The entire system integrates with Slack for alerts and AWS S3 for data storage. The solution drastically reduced inspection times and increased accuracy, while offering traceable, visual documentation that inspectors and field teams could rely on.