Artificial Intelligence for Water Quality Improvement in Bangladesh: A Review of Methods, Applications, and an Integrated Roadmap for Arsenic, Salinity, and River Pollution

<jats:p>Bangladesh faces three interlocking water quality crises: naturally occurring arsenic in shallow groundwater, salinity intrusion across the coastal southwest, and severe industrial and domestic pollution of the rivers around Dhaka. Conventional monitoring is too sparse, slow, and costly to manage these problems at national scale. This paper critically reviews how artificial intelligence (AI), and machine learning (ML) in particular, is being used to assess and improve water quality in the specific hydrological, climatic, and institutional context of Bangladesh, and asks what is still missing. We synthesise recent studies on arsenic risk mapping, coastal salinity forecasting, river water-quality-index (WQI) prediction, satellite and Internet-of-Things monitoring, and treatment optimisation, and summarise the governing equations of the dominant methods: tree ensembles, recurrent and physics-informed neural networks, and explainable AI. Reported performance commonly exceeds 0.9 (R²) for WQI regression and 0.9 accuracy for arsenic classification, yet most studies remain single-problem, single-region, and weakly validated across the monsoon cycle. We argue that the decisive gap is integration, and propose a national framework linking Bangladesh&amp;#039;s monitoring agencies, remote sensing, and low-cost sensors to interpretable models and concrete management actions. We critically examine the principal barriers (data scarcity, seasonality, transferability, interpretability, and deployment capacity) and offer a prioritised research and policy roadmap aligned with Sustainable Development Goal 6. The contribution is a Bangladesh-specific synthesis and an actionable, interpretability-centred roadmap.</jats:p>