Mapping the future: Unveiling global trends in smart waste management research

• Sharp rise in SWM publications from 2007 to 2024, marking growth in digital waste solutions. • Key trends in SWM: IoT, Smart Cities, AI, machine Learning, and circular economy. • China, USA, and India lead global SWM collaboration and research clustering. • Most organizations are in the early SWM adoption stages, signalling growth potential. • Future studies should use diverse databases and advanced methods for depth. This study aims to investigate the current landscape and collaborative dynamics of research integrating Big Data with smart waste management (SWM) through a comprehensive bibliometric analysis. We examined 2,808 scientific articles published from 2007 to 2024 using advanced bibliometric tools, VOSviewer and R-Studio (Bibliometrix R Package), to understand trends, influential contributions, and research gaps. Our findings reveal key collaboration networks, author dynamics, and international cooperation patterns, highlighting productive countries, regions, and organizations in this emerging field. The analysis of publication and citation trends shows a significant growth in research output over the studied period, indicating increased interest and investment in SWM research. Additionally, we identified the top 10 journals, authors, countries, and organizations leading in productivity. By constructing author co-citation cluster networks from Web of Science data, we mapped prominent research clusters and organizational collaborations that drive advancements in SWM, particularly within smart cities. While the findings underscore notable progress, they also reveal that many organizations remain in the early stages of SWM exploration. These insights can help researchers and policymakers strengthen collaborative networks, accelerate research development, and support practical applications of SWM solutions. We also discuss limitations in bibliometric classification methods and propose future research directions, including more in-depth content analyses of SWM and related technologies.