Computational eco-dynamics has emerged as a useful umbrella for integrating hydrochemistry, microbial ecology, source-tracing, and predictive analytics into a single environmental decision framework. In the Indian context, this convergence matters because major river basins and connected aquifers are shaped by dense human settlement, agricultural runoff, industrial discharge, monsoon variability, and rapidly changing land use, all of which create strongly coupled surface-subsurface pollution regimes that cannot be understood through single-discipline methods alone. A growing body of literature now shows that machine learning can improve microbial classification from metagenomic data, identify high-information environmental predictors, and strengthen water-quality assessment and source apportionment when combined with geospatial and hydro chemical evidence. At the same time, microbial profiling studies from the Ganga-Yamuna system demonstrate that polluted stretches contain distinct taxonomic and functional signatures, including taxa and protein domains associated with biodegradation, metal transport, and xenobiotic transformation.