<p>Taking into account that above-ground biomass (AGB) is an ecological variable, a reliable and faster approach for the estimation of AGB is crucial, especially in biodiversity-rich tropical forests for sustainable management and protection. Remote sensing (RS) technology is an important tool for this purpose. Due to the dense nature of tropical forests and persistent cloud cover in the tropics, synthetic aperture radar (SAR) RS is considered advantageous. In this review, a systematic appraisal of the recent scientific investigations that exploited SAR technology for AGB estimation of tropical forest ecosystems was carried out comprehensively. The preferred reporting items for systematic reviews and meta-analyses framework was used for this purpose and a total of 68 peer-reviewed articles were evaluated. It was noticed that most of the studies relied on space-borne datasets, in particular, ALOS PALSAR data, and the regression model was widely employed for biomass estimation. Attempts on multi-sensor and multi-temporal analysis were also observed. The advanced SAR technologies such as InSAR, polInSAR, and TomoSAR are increasingly being explored for improved AGB estimates. Predictor variables including backscattering coefficients, polarimetric decomposition parameters, texture, radar indices, and coherence gave reliable estimates. In addition, it was noticed that the LiDAR data was incorporated for canopy height information and extrapolating the field-based AGB. However, deciding on the optimum methodology or predictor variable for tropical forest AGB estimation was ambiguous due to the conflicts in the use of sensor types and predictor variables at varying AGB density indicating that AGB estimation in tropical forests is still under exploration.</p>