Advanced spatial analysis methods, including Ripley's K, L, G and F functions, investigate the spatial and temporal dynamics of drought severity and vegetation stress in arid regions. The present study models and detects multiscale clustering patterns of drought-induced vegetation stress in Saudi Arabia, an area highly vulnerable to drought due to its arid climate. The study integrates remote sensing data and Geographic Information Systems (GIS) to assess vegetation health and drought conditions. Various indices, such as the Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Vegetation Health Index (VHI), capture the complex interplay between moisture deficits, temperature stress and their effects on vegetation. The results demonstrate that drought-induced vegetation stress is not randomly distributed but forms distinct clusters, particularly in central and northern regions. Ripley's K-function reveals significant clustering of vegetation stress hotspots, with spatial patterns evolving. The recent years' shift from normal to lognormal distribution directs towards changing environmental stressors, such as intensified droughts or temperature anomalies. This study underscores the need for continuous monitoring of vegetation stress to inform water resource management and drought mitigation strategies. Integrating multiscale spatial analysis, remote sensing, and GIS offers a comprehensive approach to understanding vegetation stress dynamics in drought-prone areas. These findings provide valuable insights for policymakers to prioritize interventions and develop adaptive management strategies, ensuring sustainable agricultural productivity and ecosystem protection in the face of climate change.