All over the world, the responsible maintenance of water-related facilities is becoming popular especially because of urban growth, the impacts of climate change, and the aging of water-related facilities. Classic water engineering, although strong in the design and the hydraulic modeling parts, is still missing the resilient predictability and the real-time responsiveness. This paper tries to solve the issue from an integrated perspective, combining classical water engineering with the newest digital technologies: digital twins, IoT monitoring systems, predictive analytics with machine learning, and geo-spatial decision support systems. The first year of the integrated approach's implementation was revealing. There was a 62.5% average reduction in time taken for repairs, 96.4% reduction in leak detection time, and an increase of 26% in the accuracy for predictive failure forecasting. There was also a 42% reduction in non-revenue water and 90% reduction in decision response time. The results from the integrated approach offer a proof of concept in the pillars of operational functional, infrastructure resiliency, and data-driven decision making. It serves as a pattern to promote the smart and responsible automation of water resources.