Smart Monitoring of Air and Waste Using Machine Learning and IoT Integration Approach

This raises serious concerns for public health and environmental sustainability in an increasingly polluted atmosphere. Therefore, advanced monitoring systems must be developed. This research paper presents a novel framework that integrates Machine Learning and Internet of Things (IoT) technologies to monitor and manage air quality and waste in real time. The proposed system utilizes a network of sensors to collect high-resolution data on air pollutants such as PM2.5, PM10, NOx, and CO2, along with waste management parameters such as bin occupancy, using a publicly available dataset from Kaggle. Following rigorous data preprocessing and feature engineering, the framework achieves a peak prediction accuracy of 93.53% using an ANN. The web-based platform enables automated analysis of continuous data, allowing for immediate alerts when pollutant thresholds are exceeded facilitating timely interventions.