In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

The Earth’s terrestrial carbon stocks have depleted an estimated 344 billion tons. Carbon losses amid water scarcity in climate-vulnerable drylands are a mounting challenge, and their restoration requires optimizing water-to-carbon biotransformation. Synthesizing thousands of worldwide experimental studies, we identify key biophysical pathways for enhancing carbon restoration and ecosystem resilience in global drylands, as follows: (i) Cropping diversification increases net primary productivity by 18.9% (n = 1296 studies); (ii) Regulated deficit irrigation cuts water use by 30–50% while improving yield-scaled water use efficiency by 3.4% (n = 9068 paired comparisons); (iii) Soil mulching increases land productivity by 22.2% (n = 48,144 paired comparisons); and (iv) Soil health rejuvenation strategies can sequester 1.2–3.8 t SOC ha⁻¹ yr⁻¹. Priorities to implement these biophysical pathways to enhance water-to-carbon biotransformation include: ‘smart’ irrigation, carbon dioxide fertilization-enhanced photosynthetic assimilation, rhizosphere engineering for microbiome-based nutrient solutions, biodegradable mulches replacing traditional polyethylene films, diversifying farming systems with low soil disturbance and climate-smart practices, and inclusive governance frameworks. These prioritized strategies reconcile water scarcity with carbon restoration to enhance dryland ecosystem resilience, which supports the UN’s Sustainable Development Goals.