A Multi-dimensional AI Framework for Sustainable Drinking Water Management: Integrating Federated Learning, Digital Twins, and Blockchain

The global drinking water crisis- driven by climate change, rapid urbanization, and infrastructure gaps- demands intelligent, adaptive, and decentralized management systems. This study explores how artificial intelligence (AI) can transform conventional water governance into predictive, equitable, and sustainable frameworks. A simulation-based case study using a UCI-inspired dataset demonstrates the viability of AI models in potable water prediction: Support Vector Machines (SVM) achieved 51.4% accuracy and a 55.3% F1-score, outperforming Random Forests (accuracy 47.3%), thereby validating AI’s capacity to handle complex physicochemical classification problems even under data constraints. Building on this, the paper proposes an integrated AI framework combining federated learning for privacy-preserving collaboration, edge AI for low-connectivity environments, AutoML for usability by non-experts, and blockchain for tamper-proof water quality certification. Digital twins are incorporated to simulate water infrastructure behavior under real-time operational scenarios. The framework further extends to advanced AI paradigms- including adaptive, graph-based, and multimodal learning systems- used to optimize distribution networks, detect anomalies, and support climate-sensitive water forecasting. To guide policy and track development goals, an AI-driven SDG metrics dashboard is proposed for real-time assessment of progress toward SDG 6.1 (safe and affordable drinking water) and SDG 6.3 (improved water quality). Ethical dimensions such as data privacy, transparency, and explainable AI (XAI) are emphasized to ensure trustworthy deployment, especially in climate-vulnerable or under-resourced regions. While the results are based on synthetic data modeled after open-source benchmarks, the approach presents a scalable template for future field integration. This research demonstrates that AI introduces a new affordance in global water governance, enabling systems that are not only smarter and faster but also more inclusive, resilient, and accountable in addressing 21st-century water challenges.