Efficient and timely transportation of blood samples across medical centers is critically important, particularly in countries where traffic congestion significantly delays road-based delivery. The operational range of the unmanned aerial vehicles remains constrained by limited battery capacity and variable energy consumption across different terrains. This paper proposes an integrated approach for optimizing drone-based medical transportation by combining a 3D enhanced A* pathfinding algorithm with a K-means clustering method for strategic deployment of drone charging stations. The 3D A* algorithm incorporates elevation and environmental obstacles, enabling accurate estimation of flight costs. Charging station placement considers population density, topography, power infrastructure, and drone power consumption models. A case study involving Lebanese hospitals demonstrates the effectiveness of the proposed system in minimizing flight distance, reducing energy consumption, and ensuring mission feasibility under real-world constraints.

The GIS datasets used in this study were obtained from publicly available sources, including USGS EarthExplorer, Open Data Lebanon, the Lebanese Ministry of Public Health, and IGISMap. The datasets were processed and converted into geospatial shapefiles for use in the UAV path-planning simulations. The processed datasets and derived shapefiles are available from the corresponding author upon reasonable request, in accordance with the journal’s data-sharing guidelines.

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