Ali, GumaMijwil, Maad M.Adamopoulo, IoannisDhoska, Klodian2025-12-182025-12-182025-09-01Ali, G., Mijwil, M. M., Adamopoulos, I., & Dhoska, K. (2025). Blockchain and quantum machine learning approach for securing smart water management systems: A Scoping review. SHIFRA, 2025, 141-202.3078-3186https://dir.muni.ac.ug/handle/20.500.12260/839This scoping review examines 266 studies (Jan 2022–Dec 2025) on securing smart water management systems using Blockchain and quantum machine learning (QML), highlighting their role in enhancing secure data logging, access control, anomaly detection, predictive maintenance, and distribution optimization. It links strongly to SDG 6 (clean water and sanitation), SDG 9 (industry, innovation, infrastructure), and SDG 11 (sustainable communities) by promoting resilient, efficient water infrastructure. The emphasis on scalable, privacy-preserving frameworks aligns with Uganda’s NDP IV goals of industrialization, infrastructure development, and sustainable water resource expansion, helping achieve household monetization, economic transformation, and improved public services.Smart water management systems (SWMS) increasingly rely on Internet of Things (IoT) devices to enhance water distribution, detect leaks, and support sustainable resource use, but this reliance also heightens exposure to cyberattacks, data manipulation, and privacy risks. Conventional security approaches often fall short due to the decentralized design and real-time demands of these systems. This scoping review analyzes 266 studies published between January 2022 and December 2025 to assess how integrating Blockchain and quantum machine learning (QML) can strengthen the security, privacy, and reliability of SWMS. The review examines Blockchain-enabled water management, quantum computing applications, and QML-based security frameworks, using thematic analysis to categorize emerging architectures and challenges. Findings of the focused studies show growing adoption of Blockchain for secure data logging, access control, and tamper-proof auditing. At the same time, QML demonstrates strong potential in anomaly detection, predictive maintenance, and optimizing distribution networks. Although these technologies offer a promising foundation for resilient water infrastructure, most research remains conceptual, with limited real-world deployment or scalability assessments. Integrating Blockchain with QML could create robust, privacy-preserving SWMS frameworks. However, significant barriers persist, including the computational intensity of quantum models, interoperability issues with existing IoT infrastructures, and the absence of standardized protocols. Addressing these gaps is essential for practical implementation. This review underscores the need for scalable hybrid designs, applied validation, and cross-disciplinary standards to advance secure, efficient, and sustainable smart water management solutions.enSmart water managementBlockchainquantum machine learningcybersecurityLOTscoping reviewArticle