Maintaining safe drinking water requires detecting trace-level amounts of harmful contaminants quickly. However, many water systems use laboratory tests that are slow and expensive. This CAREER project will create a faster, low-cost way to check drinking water for harmful chemicals by combining a chemical sensor with artificial intelligence (AI). The project will use a method that measures how light is scattered from chemicals attached to a surface. Each chemical generates its own pattern, like a fingerprint, that can be read. Drinking water may contain a collection of chemicals. The project will use AI to help read and understand the test results. The project will study how real-world conditions such as acidity and salts in the water affect how well the method works. The detection system will quickly flag possible contamination, so water utilities can do follow-up testing. The project will also use computer models to better understand how chemicals interact with the sensor, which will help improve the sensor design. Project outcomes will help protect public health and lower the cost of water testing. The project will also provide research opportunities to college students and high school teachers, create teaching materials about water sensors and AI, and offer hands-on outreach activities about drinking water quality problems. This CAREER project will develop a deep learning-enabled surface-enhanced Raman spectroscopy (SERS) platform for rapid, low-cost, and quantitat