Beyond Hydrochemical Indices: Integrating GIS–AI Decision Frameworks to Irrigation Water Quality for Climate‐Resilient Soil–Water–Crop Systems and Sustainable Drainage

Irrigation water quality is a critical engineering determinant of soil health, drainage efficiency and crop resilience, particularly in arid and semi‐arid agroecosystems. Classical hydrochemical indices such as electrical conductivity (EC), sodium adsorption ratio (SAR) and residual sodium carbonate (RSC) remain indispensable for diagnosing salinity and sodicity hazards, yet they inadequately capture the complexity of modern irrigation systems shaped by climate variability, unconventional water sources and emerging contaminants. This study advances irrigation water quality assessment by integrating hydrochemical diagnostics with geospatial and artificial intelligence (GIS–AI) decision frameworks. This review critically evaluates engineering implications of salinity, sodicity and contaminant loads and demonstrates how GIS–AI workflows enable predictive, spatially explicit monitoring of soil–water–crop interactions. Case studies from Egypt illustrate applications in irrigation scheduling, salinity risk zoning and drainage planning under climate‐induced stress. Findings reveal persistent gaps in linking classical indices with system‐level models and governance frameworks, underscoring the need for adaptive, intelligent approaches. We propose a multidimensional paradigm where irrigation water quality is treated not only as a chemical constraint but also as an engineering cornerstone for climate‐resilient drainage and sustainable agroecosystems.