The accelerating pressures of population growth, industrial expansion, climate variability, and urbanization have rendered water quality monitoring and management one of the most critical challenges of the twenty-first century. Traditional water monitoring frameworks, largely reliant on periodic sampling and centralized laboratory analysis, are increasingly inadequate to address the spatial and temporal complexity of contemporary water systems. In response, a convergence of emerging technologies—biosensors, Internet of Things architectures, artificial intelligence, and blockchain—has begun to redefine how water quality data are generated, transmitted, analyzed, governed, and trusted. This article presents a comprehensive and theoretically grounded examination of integrated smart water quality monitoring systems, synthesizing insights from environmental science, information systems, and sustainability studies. Drawing strictly on the provided body of literature, the paper critically analyzes the principles and applications of biosensors for environmental monitoring, the evolution of water quality monitoring strategies, the role of IoT-enabled sensor networks in real-time data acquisition, and the transformative potential of artificial intelligence for predictive analytics and decision support in water governance (Huang et al., 2023; Behmel et al., 2016; Hoang et al., 2022). Particular emphasis is placed on blockchain technology as an institutional and technical mechanism for enhancing data integrity, transparency, and accountability in distributed water management systems, especially in contexts characterized by fragmented governance and trust deficits (Nofer et al., 2017; Xia et al., 2022).

Beyond technological integration, this article situates smart water monitoring within broader socio-economic and regulatory frameworks, including the Clean Water Act, national drinking water standards, and sustainable development imperatives (Keiser and Shapiro, 2019; BIS, 2012; Huang et al., 2023). The methodological approach adopts a critical interpretive synthesis of interdisciplinary literature to construct an integrated conceptual framework that elucidates how sensor-level innovations scale into system-level sustainability outcomes. Results are articulated through descriptive and interpretive analysis rather than quantitative modeling, highlighting patterns, alignments, and tensions across scholarly perspectives. The discussion advances a nuanced critique of techno-centric narratives by foregrounding issues of equity, governance, cybersecurity, and rural–urban disparities in water access and monitoring capacity (Pacheco et al., 2017; Yasin et al., 2021). The article concludes by identifying future research directions that prioritize socio-technical co-design, regulatory harmonization, and ethical data stewardship. Collectively, the study contributes a publication-ready, theoretically expansive, and policy-relevant foundation for advancing secure, intelligent, and sustainable water quality monitoring systems aligned with global development goals.

Water quality monitoring, Biosensors, Internet of Things, Blockchain technology, Artificial intelligence, Sustainable development, Smart water management

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