Wireless Sensor Networks (WSNs) are becoming very promising solutions on the Internet of Things (IoT) paradigm for environmental observing. This study uses Decision Tree (DT) algorithms to examine WSNs for environmental management, water quality monitoring, and air quality monitoring. WSNs monitor air quality by measuring temperature, humidity, particle matter, pollutant gases, and atmospheric pressure. WSNs also measure pH, dissolved oxygen, turbidity, and stream pollution to assess water quality. DT algorithm classifies the acquired data to identify anomalies and predict the occurrences of pollution. WSNs and DT provide continuous navigation and provide air and water pollution response at the earliest. This work covers WSNs for environmental monitoring using IoT, focusing on water quality, and air quality. DT algorithms for data processing provide real-time choices, early environmental problem detection, and improved resource management. © 2025 Elsevier B.V., All rights reserved.