Drinking water is important for our health. Disinfection helps keep the water safe and clean. However, some disinfection byproducts (DBPs) can be harmful. These DBPs form when water treatment plants use chemicals like chlorine to kill germs. There are many different DBPs, and identifying all of them would be costly and difficult for water treatment plants. This project will use machine learning (ML) to identify the toxicity of DBPs in drinking water and develop strategies for reducing the presence of high-risk DBPs in drinking water treatment. The results from the project will improve drinking water safety and public health. The project will train students to use AI and data to solve problems of water quality. The project will mentor graduate and undergraduate students at South Dakota School of Mines and Technology, preparing them for future careers in science, engineering, and data science. Disinfection byproducts (DBPs) are a group of chemicals formed during the water disinfection process when disinfectants such as chlorine react with organic matter in water. These chemicals are often toxic and present in treated drinking water. The considerable number of DBPs, coupled with limited data on their occurrence and toxicity, complicates efforts to determine which DBPs should be prioritized for future studies and regulations. Current methods for assessing DBP risks rely on limited occurrence and toxicological data and thus face challenges in effectively identifying which DBPs