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The assessment of urban ecological quality through remote sensing has gained prominence in recent years, due to the need for effective urban monitoring and improved territorial planning. This study presents a comprehensive review of the evolution of urban ecological-quality research from 1997 to 2023, focusing on trends, influential publications, and methodologies. From 1997 to 2023, research on urban ecological quality grew significantly, with annual publications increasing from 0.3 in the 1990s to six in the 2020s, driven by technological advancements, global collaboration, and alignment with policy goals like the UN Sustainable Development Goals (SDGs). Co-occurrence network analysis revealed six key research clusters, highlighting advancements in methodologies, spatial data integration, remote sensing, green sustainability, and multi-criteria frameworks, showcasing the field’s interdisciplinary evolution. China leads contributions, with 33.3% of research, followed by the United States and other countries, emphasizing robust international collaborations. Journals like Remote Sensing and Sustainability dominate, with highly cited publications from the 2010s and 2020s shaping the field’s direction. Prominent authors such as Xu H. and Zhang X. have played critical roles, though engagement in the field has surged more recently. Remote-sensing technologies, particularly in China, have been pivotal, with indices like the Remote-Sensing Ecological Index (RSEI) and its derivatives broadening analytical frameworks. These tools integrate ecological, socio-economic, and policy dimensions, aligning with global sustainability objectives and enhancing the field’s capacity to address urban ecological challenges and promote sustainable urban development. Urban ecological-quality research has evolved significantly, driven by advancements in remote sensing, interdisciplinary methods, and global collaboration. Future efforts should focus on expanding cross-regional studies, integrating comprehensive socio-economic and environmental indicators, and utilizing emerging technologies like machine learning, deep learning, and AI to address urbanization challenges and support sustainable development.