IoT-enabled technologies have advanced so that smart sensor systems can observe and recognize human behavior in various contexts, including energy consumption and healthcare, with remarkable efficiency and effectiveness. One example is using the Internet of Things (IoT) technology to better comprehend human water consumption behavior and establish and maintain clean environments. Static models have typically been used to model the behavior intervention process throughout time. While these static approaches perform adequately when predicting general human behavior, they fall short when tracking and reacting to shifts in behavior in IoT settings. The authors of this study proposed a dynamic behavior intervention model to forecast the hygiene-related water-use habits of individual households. This model takes its cues from the structure equation model method and the notion of control engineering, which originated in the expanded theory of planned behavior (ETPB). The current ETPB dynamic behavior model with system parameter estimation using an artificial neural network (ANN) is assessed for its intervention trend using a residential water use case study. It has been shown that the ETPB dynamic model helps the process of intervening in people’s behavior.