Clean and safe water is a fundamental human right, yet achieving Sustainable Development Goal 6 (SDG 6) ensuring water and sanitation for all remains a persistent global challenge due to pollution, climate stress, limited resources, and aging infrastructure. This study presents a novel, end-to-end AI-quantum hybrid framework that advances water purification, pollution control, and infrastructure monitoring beyond the scope of existing methods. Unlike previous works, which typically focus on isolated tasks or singular model types, we propose the first unified benchmarking pipeline that systematically compares classical, deep learning, and quantum-enhanced models across ten real-world smart water management tasks, including pollutant forecasting, leak detection, microbial risk classification, and anomaly detection. Leveraging large-scale environmental datasets, our models predict contamination trends, optimize treatment protocols, and enable real-time health monitoring of water systems. Quantum models such as Quantum Graph Neural Networks (QGNN), variational quantum circuits, and hybrid CNNs capture high-dimensional, nonlinear relationships that classical models often fail to learn. To enhance transparency and policy integration, we incorporate visualization-driven interpretability tools and ethics-aware deployment strategies. Case studies in arsenic mitigation, heavy metal detection, and microbial purification demonstrate the framework’s real-world applicability and scalability. Overall, this work offers a first-of-its-kind, modular, and ethically aligned AI-quantum architecture designed for resilient and adaptive smart water systems, accelerating measurable progress toward SDG 6, particularly in underserved and resource-constrained regions.