<p>Mangrove ecosystems are central to global climate mitigation and coastal resilience, yet their monitoring remains inherently complex due to tidal inundation, spectral ambiguity and persistent cloud cover across tropical regions. The emergence of cloud-based geospatial platforms, particularly the Google Earth Engine (GEE), has transformed mangrove remote sensing from scene-based analysis to scalable, multi-sensor, time-series frameworks operating at planetary scale. This study presents a systematic review and critical synthesis of 162 peer-reviewed studies published between 2017 and 2025, examining the structural evolution, methodological foundations and ecological implications of GEE-enabled mangrove research. Bibliometric assessment indicates rapid expansion of the field, reflecting a transition from early methodological experimentation to widespread application. Thematic evolution reveals a clear shift from two-dimensional extent mapping to increasingly dynamic and process-oriented analyses, including disturbance monitoring, hydroperiod assessment, biomass estimation and blue carbon quantification. Methodologically, the literature is strongly anchored in ensemble machine learning approaches, particularly Random Forest, supported by multi-sensor integration of optical time-series (Landsat, Sentinel-2) and Synthetic Aperture Radar (SAR) to mitigate atmospheric and tidal constraints. Despite these advances, critical structural limitations persist. Research output and study-area focus remain geographically concentrated in a limited number of countries, whereas several ecologically significant mangrove regions remain underrepresented, indicating a mismatch between research capacity and ecosystem distribution. Furthermore, although classification accuracy has improved substantially, persistent uncertainties arising from tidal variability, spectral similarity, training data bias and limited model transferability constrain ecological interpretation. These findings indicate that GEE has successfully enabled large-scale mangrove monitoring but has not yet fully translated computational advances into ecological understanding or operational decision-making. Future research should prioritize cross-regional model validation, the integration of structural datasets such as spaceborne light detection and ranging (LiDAR), and the development of interpretable, process-based frameworks to better connect remote sensing outputs with ecosystem functioning. Strengthening these directions is essential to ensure that GEE-based monitoring systems effectively contribute to global mangrove conservation, blue carbon accounting and climate-resilient coastal management.</p>