BACKGROUND AND OBJECTIVES: The rapid deterioration of water quality in large tropical reservoirs threatens ecological integrity, water security, and socio-economic stability, particularly in developing regions with limited environmental monitoring capacity. Conventional approaches to water quality monitoring, predominantly based on manual sampling and stationary sensors, face limitations due to poor spatial and temporal resolution, which often obstructs timely detection and management of pollution incidents. The aims of the study were to create and validate a comprehensive, adaptive real-time monitoring system that combines static, stratified, and mobile sensing approaches into a scalable framework for sustainable reservoir management.METHODS: Sixteen static (fixed-location) monitoring stations were strategically deployed across the Jatiluhur Reservoir, Indonesia’s largest multipurpose water body, to continuously measure key parameters including dissolved oxygen, temperature, potential of Hydrogen, turbidity, and conductivity. Real-time data transmission was facilitated through Global System for Mobile based communication networks linked to a central analytical platform, where sensor data were verified against laboratory instruments that meet International Organization for Standardization-standard laboratory instruments to ensure accuracy and dependability.FINDINGS: The system demonstrated an average operational reliability of ۹۲.۴ percent across sixteen monitoring stations, with eight stations consistently achieving data availability rates above ۹۰ percent. It effectively identified hypoxic regions where dissolved oxygen concentrations fell below ۲.۵ milligrams per liter, turbidity levels surpassed ۳۰ nephelometric turbidity units, and temperature stratification differences reached as high as ۶ degrees Celsius between the surface and bottom layers during upwelling periods. Comparative analysis with international benchmarks revealed that the integrated system improved data consistency by ۱۲–۱۵ percent and reduced response time by ۳۵ percent relative to conventional monitoring systems. The integration of real-time validation and AI-assisted analytics further enhanced predictive capability, achieving correlation coefficients greater than ۰.۹۲ between field sensors and International Standart Organization standard laboratory measurements. CONCLUSION: The integrated real-time monitoring system provides a replicable model for adaptive and sustainable reservoir governance, supporting both environmental protection and operational efficiency. The system's execution aids in the formulation of environmentally sustainable policies based on data and effective reservoir management strategies, aligning with international water governance standards. These findings demonstrate the system’s potential as a critical tool for achieving long-term water resource sustainability in tropical developing regions.