Clean and reliable water treatment is critical to protect public health. However, most treatment decisions still rely on simple models that do not reflect real‑world conditions. Artificial intelligence (AI) has the potential to help engineers make more informed decisions. This CAREER project will develop AI models that help engineers understand how and why treatment processes work at full-scale. The project will reduce risk, improve reliability, and expand access to advanced tools for communities with limited technical resources. The outcomes of this research will be shared with water utilities and used across many treatment systems. The project will also address a national need for a workforce that can use AI responsibly by integrating data science into environmental engineering education. This project will support safer water systems, prepare future engineers, and show how AI can be used as a tool for scientific discovery. This CAREER project will develop an application‑driven AI framework for modeling engineered environmental systems, using water and wastewater disinfection as a representative, high‑risk unit process. The research will integrate multi‑facility operational and water quality data with hybrid modeling approaches that combine physics‑based process models and machine learning (ML). These approaches will include mechanistic ordinary differential equation models coupled with ML components, physics‑informed neural networks, and embedded neural differential equa