Abstract Satellite thermal infrared (TIR) observations, combined with machine‐learning methods, enable nighttime retrievals of cloud optical thickness (COT), but their cloud‐top‐dominated radiances provide ambiguous information for optically thick clouds. Passive microwave (MW) observations provide complementary sensitivity to column‐integrated liquid/ice clouds. Here we quantify this benefit by combining TIR and MW brightness temperatures within a U‐Net COT retrieval framework. Relative to a TIR‐only baseline, adding MW channels (TIR‐MW) increases the correlation with MODIS MYD06 COT from 0.70 to 0.77, and reduces mean absolute error by 18.3% on an independent test set. Improvements are largest for optically moderate‐to‐large clouds, where TIR‐only retrievals suffer from cloud‐top saturation and structural ambiguity. Results demonstrate that TIR‐MW synergy provides physically complementary, column‐integrated constraints that improve passive COT retrievals, strengthen the physical basis of machine‐learning cloud property retrievals, and reduce their tendency to underestimate optically thick clouds. This TIR‐MW integration strategy provides a physically grounded pathway for all‐day cloud‐property retrievals.