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Forest height (FH) is an important driver for aboveground biomass (AGB) that can be obtained using interferometric synthetic aperture radar (InSAR). However, the limited access to the quad-polarimetric data or high-accuracy terrain model makes FH retrieval a challenging task. This study aimed to retrieve FH and further predict AGB by combining TanDEM-X InSAR coherence, Sentinel-1 (S-1), and Sentinel-2 (S-2) data. A total of 125 sample plots with a size of 900 m² were established in a broadleaved forest of Kheyroud, Iran. The linear and sinc models obtained by simplification of the random volume over ground model were used for deriving FH_Lin and FH_Sinc. Further investigation was conducted when S-1 and S-2 features including backscatters and multispectral information were added to FH predictions. Using the above-mentioned datasets and FH as an additional predictor, AGB was also predicted. <italic>K</italic>-nearest neighbor (<italic>k</italic>-NN), random forest (RF), and support vector regression (SVR) were employed for prediction. Lorey&#x0027;s mean height and AGB at sample plots were used in the accuracy assessment. Using the SVR method and synergy of FH_Sinc, S-1, and S-2 features, the FH prediction was improved (FH_imp) with RMSE of 3.18 m and <italic>R</italic>² &#x003D; 0.59. The AGB prediction with RF and the combination of S-1 and S-2 features resulted in RMSE &#x003D; 62.88 Mg&#x00B7;ha^-1 (19.77&#x0025;) that was improved to RMSE &#x003D; 51.27 Mg&#x00B7;ha^-1 (16.12&#x0025;) when FH_imp included. This study highlighted the capability of TanDEM-X InSAR coherence with certain geometry for FH prediction. Also, the importance of FH in AGB predictions can stimulate further attempts aiming at higher spatiotemporal accuracies.