Urban forest management requires accurate and scalable methods for monitoring tree structure and health. Traditional inventory approaches are labor-intensive and limited in spatial coverage, while existing remote sensing methods often lack the resolution and adaptability needed for diverse urban environments. This study presents an automated UAV-based framework that integrates machine learning, image processing, and topographical analysis for individual tree detection and characterization. The framework employs YOLOv8 object detection for initial tree identification, followed by slope-based region-growing segmentation that refines canopy boundaries using Digital Elevation Model (DEM) and Digital Terrain Model (DTM) data. A two-stage Otsu's thresholding approach addresses small tree exclusion in mixed height canopies. Tree structural attributes including height and canopy girth, are derived from DEM/DTM analysis, while vegetation health is assessed using the Modified Green-Red Vegetation Index (MGRVI). The methodology was validated on UAV datasets from Maharashtra and Telangana, achieving 95% detection accuracy in sparse canopy regions and 85% in dense areas. An interactive WebGIS interface enables spatial visualization of tree attributes and health metrics to support data-driven urban forestry decisions. The framework demonstrates adaptability across diverse tree species and canopy densities, offering a scalable solution for automated urban forest monitoring and management.