The accurate determination of the location, height, and loading of sulfur dioxide (SO.sub.2) plumes emitted by volcanic eruptions is essential for aviation safety. The SO.sub.2 layer height is also one of the most critical parameters with respect to determining the impact on the climate. Retrievals of SO.sub.2 plume height have been carried out using satellite UV backscatter measurements, but, until now, such algorithms are very time-consuming. We have developed an extremely fast yet accurate SO.sub.2 layer height retrieval using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm. This is the first time the algorithm has been applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform. In this paper, we demonstrate the ability of the FP_ILM algorithm to retrieve SO.sub.2 plume layer heights in near-real-time applications with an accuracy of better than 2 km for SO.sub.2 total columns larger than 20 DU. We present SO.sub.2 layer height results for the volcanic eruptions of Sinabung in February 2018, Sierra Negra in June 2018, and Raikoke in June 2019, observed by TROPOMI.