Accurate geological structural mapping remains a critical requirement for safe, efficient, and economically viable mining operations, particularly in underground and open-pit environments characterized by irregular geometries and hazardous access conditions. Traditional surveying techniques, though reliable, are constrained by line-of-sight limitations, safety risks, and insufficient data density for modelling increasingly complex ore bodies. Autonomous drones provide a transformative alternative, offering continuous, high-resolution data acquisition with minimal human exposure. However, realizing their full potential requires deployment strategies that optimize flight paths, sensor positioning, environmental adaptation, and data-fusion efficiency. This study examines how autonomous drone systems can be strategically deployed to maximize structural-mapping accuracy in complex mines. The analysis begins by reviewing the challenges posed by variable lighting, dust interference, constrained tunnels, GPS-denied zones, and unstable geological surfaces. It then evaluates advanced autonomy modules including SLAM-based navigation, adaptive obstacle avoidance, and multi-sensor calibration to determine how these capabilities influence mapping fidelity. A key emphasis is placed on optimizing flight-trajectory planning using probabilistic models, geospatial uncertainty reduction techniques, and AI-driven waypoint selection tailored to local geological complexity. The study further investigates how multi-modal sensor integration, including LiDAR, hyperspectral imaging, photogrammetry, and magnetometric sensing, enhances the detection of fault planes, discontinuities, joint sets, and micro-fracture networks. Simulation-driven optimization approaches are compared with field-tested deployment patterns to establish best-practice strategies for achieving comprehensive spatial coverage with minimal redundancy. The findings demonstrate that optimized autonomous-drone deployment significantly improves structural-mapping resolution, reduces data-collection time, and enhances operational safety ultimately enabling mining organizations to make faster, more reliable, and data-driven geotechnical decisions.