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Urban stormwater management is increasingly a challenge due to land use change, aging infrastructure, and climate‐driven precipitation variability. Likewise, maintaining regulatory compliance for stormwater permits is becoming more difficult. This study develops and deploys stormwater sensors using an Internet of Things‐based monitoring framework on the University of Maryland campus, a spatially compact but land use diverse testbed, designed to support both compliance and adaptive planning. Across three campus outfalls for stormwater quantity and quality data collection, the study investigates how hyperlocal precipitation and catchment characteristics affect stormwater flow and identifies key patterns in stormwater flow and quality through continuous monitoring. Findings reveal correlations between runoff behaviors and catchment characteristics (i.e., imperviousness) and highlight site‐specific associations between runoff flow and water quality indicators (pH, turbidity, conductivity, and dissolved oxygen). These associations can be leveraged as indicators of flood and pollution risk for management and planning purposes. This study also explores the role of campus stakeholders in guiding a “smart” system design, deployment, and big data use and outlines adaptive and preventive strategies for mitigating field deployment challenges and optimizing system performance that is a practical, compliance‐oriented model for smart stormwater monitoring in complex urban settings at various scales.