Environmental and climate change monitoring in edge computing has become an important research area, aiming to enable real-time, distributed, and intelligent observation of environmental parameters while minimizing data transmission to centralized cloud systems. Research papers in this domain explore the deployment of IoT sensors, edge devices, and drones for monitoring air quality, water resources, soil conditions, greenhouse gases, weather patterns, and other climate-related indicators. Studies focus on low-latency data processing, adaptive resource allocation, and energy-efficient analytics at the edge to support timely decision-making and automated control actions. Recent works integrate machine learning, deep learning, and federated learning techniques for predictive modeling, anomaly detection, and environmental trend analysis directly at edge nodes. Security- and privacy-preserving frameworks are also emphasized to safeguard sensitive geospatial and environmental data. Additionally, hierarchical edge–fog–cloud architectures are studied to improve scalability, reliability, and resilience in large-scale environmental monitoring networks. Overall, edge computing for environmental and climate change monitoring enables intelligent, real-time, and sustainable solutions for proactive environmental management and climate mitigation strategies.