High spatiotemporal resolution rainfall is needed in predicting flash floods, local climate impact studies and agriculture management. Rainfall estimation techniques like satellites and the commercial microwave links (MWL) rainfall estimation have independently made significant advancements in high spatiotemporal resolution rainfall estimation. However, their combination for rainfall estimation has received little attention, while it could benefit many applications in ungauged areas. This study investigated the usability of the random forest (RF) algorithm trained with MWL rainfall and Meteosat Second Generation (MSG) based cloud top properties for estimating high spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift Valley. Our approach retrieved cloud top properties for use as predictor variables from rain areas estimated from the MSG data and estimated path average rainfall intensities from the MWL to serve as the target variable. We trained and validated the RF algorithm using parameters derived through optimal parameter tuning. The RF rainfall intensity estimates were compared with gauge, MWL, Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to evaluate its rainfall intensities from point and spatial perspectives. The results can be described as good, considering they were achieved in near real-time, pointing towards a promising rainfall estimation alternative based on the RF algorithm applied to MWL and MSG data. The applicative benefits of this technique could be huge, considering that many ungauged areas have a growing MWL network and MSG and, in the future, Meteosat Third Generation coverage.