Integration of GIS, Big Data, and Artificial Intelligence in Modern Waste Management Systems—A Comprehensive Review

This article presents a narrative, traditional literature review summarizing current research on the integration of digital technologies in waste management. The study examines how intelligent technologies, including Geographic Information Systems, Big Data analytics, and artificial intelligence, can improve energy efficiency, support sustainable resource use, and enhance the development of low emission and circular waste management systems. The reviewed research shows that the combination of spatial analysis, large-scale data processing, and predictive computational methods enables advanced modeling of waste distribution, the optimization of collection routes, intelligent sorting, and the forecasting of waste generation. Geographic Information Systems support spatial planning, site selection for waste facilities, and environmental assessment. Big Data analytics allows the integration of information from Internet of Things sensors, global positioning systems, municipal databases, and environmental registries, which strengthens evidence-based decision making. Artificial intelligence contributes to automatic classification, predictive scheduling, robotic sorting, and the optimization of recycling and energy recovery processes. The study emphasizes that the integration of these technologies forms a foundation for intelligent waste management systems that reduce emissions, improve operational efficiency, and support sustainable urban development.