Urban settings are facing serious issues due to the increasing levels of air and noise pollution, which have an influence on public health and quality of life. In order to successfully address these difficulties, this work discusses the creation of an improved air and noise pollution monitoring system using Internet of Things (IoT) technology. A network of Internet of Things-enabled sensors is integrated into the system to continually monitor noise levels and air quality in real-time. The suggested system consists of noise sensors that record decibel levels and air quality sensors that detect important pollutants such particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3). These sensors wirelessly provide data to a central data processing unit, where cloud-based analytics tools are used for analysis and visualization. The IoT architecture offers extensive coverage and in-depth environmental information, enabling scalable implementation across several sites. Real-time data collecting capabilities in the system allow for prompt warnings and alerts about pollution levels. Tools for data analytics and visualization provide customers with an easy-to-use interface for tracking patterns, evaluating the causes of pollution, and making defensible judgments. Furthermore, the use of machine learning algorithms improves prediction capacities by projecting pollution patterns via the use of past data. The system's efficacy in delivering precise, real-time monitoring and useful insights is shown by preliminary findings. This cutting-edge IoT-based strategy provides a reliable way to control noise and air pollution, enabling improved public awareness, policy-making, and urban planning. The paper emphasizes how IoT technology might improve urban sustainability and advance environmental monitoring.

Urban settings are facing serious issues due to the increasing levels of air and noise pollution, which have an influence on public health and quality of life.