Data centres support artificial intelligence (AI) development but place rapidly increasing demands on electricity and freshwater resources, with cooling representing a significant portion of their total energy consumption. Wastewater treatment plants (WWTPs) discharge large volumes of treated effluent with substantial cooling potential; however, their integration with data centre infrastructure has not been evaluated. Here we construct a global geodatabase of over 4775 data centres and 57,547 municipal WWTPs across 98 countries, integrating spatial analysis, engineering systems modelling, optimisation, and life-cycle assessment to quantify the benefits of combining treated water reuse with bidirectional thermal recovery. The analysis reveals a strong global spatial co-occurrence between data centres and WWTPs, enabling optimized national-scale pairings in which treated effluent is used for data centre cooling and the return heat is recovered to support sludge drying and anaerobic digestion. This symbiotic approach reduces greenhouse gas emissions by approximately 84 million tonnes of CO₂ equivalent annually, conserves approximately 1300 million m³ of freshwater, and provides net annual cost savings of approximately US＄95.4 billion. The greatest mitigation and water-saving potential lies in the United States, Japan, China, the Netherlands, and the United Kingdom. These findings establish data–water symbiosis as a readily scalable infrastructure solution that decouples AI from its carbon and water footprints. WWTPs are poised to evolve from disposal facilities into critical energy-coupling hubs, enabling efficient thermal and water exchange across urban systems and accelerating progress towards multiple Sustainable Development Goals.

The parameters of activatedg sludge are crucial for the daily operation of wastewater treatment plants (WWTPs). In particular, mixed liquor suspended solids (MLSS) and apparent viscosity provide metrics for the biomass and rheological properties of activated sludge. Traditional methods for determining these parameters are time-consuming, require separate measurements for each index, and fail to provide real-time data for future ‘smart’ WWTPs. Here we show a real-time online microscopic image data analysis system that quantitatively identifies MLSS and apparent viscosity. Microscopic videos of activated sludge are captured in lab-scale sequencing batch reactors under chemical oxygen demand shock, yielding 41482 high-quality images. The Xception convolutional neural network architecture is used to establish both qualitative and quantitative correlations between these microscopic images and MLSS/apparent viscosity. The accuracies of qualitative identification for MLSS and apparent viscosity are both higher than 97%, and the quantitative correlation coefficients are 0.95 and 0.96, respectively. This quantitative correlation between microscopic images of activated sludge and its physical parameters, specifically MLSS and apparent viscosity, provides a basis for real-time online measurements of activated sludge parameters in WWTPs.