The integration of trend-based Environmental Stress Severity (ESS) and Artificial Neural Network (ANN) modeling provides a robust framework for quantifying natural environment dynamics and forecasting stress in arid ecosystems. This study focuses on the Makkah Watersheds, Saudi Arabia. ESS was derived from Kendall's tau and significance thresholds applied to long-term Landsat indices (NDVI, NDWI, LST; 1991-2023) and hydroclimatic drivers from TerraClimate (temperature, precipitation, AET, CWD). Trend analysis revealed significant declines in NDVI (20.48 km(2)) and NDWI (99.57 km(2)) and an increase in LST (156.5 km(2)) at p < 0.05. Hydroclimatic pressures intensified as CWD expanded (1,936 km(2)) while AET (176 km(2)) and precipitation (128 km(2)) contracted. The optimized ANN models demonstrated strong predictive performance for Environmental Stress Severity, with the baseline ANN achieving R-2 = 0.881 (MAE = 0.200), and further improvements obtained using metaheuristic optimization (ANN-PSO: R-2 = 0.890, MAE = 0.186; ANN-GWO: R-2 = 0.883, MAE = 0.196). Zonal and landcover diagnostics showed the highest stress in sparsely vegetated zones (e.g., WS5: 3.49 +/- 0.67, WS4: 3.31 +/- 0.71), whereas built-up areas exhibited lower means (e.g., WS1: 2.88 +/- 0.72). Right-skewed ESS distributions in natural covers highlight asymmetric dryland vulnerability, characterized by a larger tail of highly stressed pixels. The workflow is scalable, temporally sensitive, and directly actionable for restoration planning, land-use regulation, and climate-risk adaptation in drylands.