Advances in remote sensing have revolutionized the monitoring of alpine vegetation, providing crucial insights into these fragile mountain ecosystems and their medicinal plant species. This chapter examines the evolution of remote sensing technologies ranging from satellite, UAV, airborne, and ground-based platforms to advanced sensor types including optical, multispectral, hyperspectral, radar, and thermal systems for comprehensive alpine vegetation analysis. Emphasis is placed on the integration of diverse datasets, such as high-resolution imagery, LiDAR, gridded climate data, and derived vegetation indices, which enable detailed mapping, species discrimination, habitat assessment, and phenological tracking at multiple spatial and temporal scales. The chapter further explores the implementation of cutting-edge AI-driven image processing, feature extraction, classification, and data fusion methodologies, highlighting developments in machine learning, deep learning, and fusion algorithms that enhance the accuracy and ecological interpretation of remote sensing outputs for alpine environments. Key applications for effective ecosystem management and conservation included are biodiversity assessment, monitoring vegetation dynamics in response to climate change, high-precision vegetation cover estimation, productivity modeling, and habitat classification. The chapter concludes by addressing prevailing challenges such as data acquisition constraints, technical and cost barriers, and the need for open-access, standardized approaches, outlining innovative future directions to ensure remote sensing fulfills its transformative potential in safeguarding alpine vegetation.