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Successful tea cultivation is dependent on careful soil management practices that are underpinned by information on soil properties, which tends to be sparse in tea growing regions. Such information is often periodically captured through field sampling, however, may only be available for selected plantations or fields within larger tea estates. Consequently, soil management decisions on some plantations are made in the absence of soil information.In this study, digital soil mapping techniques were used to create 30 m resolution maps of selected soil properties that were captured within Kericho, Kimugu and Cheymen tea plantations on a large tea estate managed by Browns East Africa Plantations Kenya Ltd in Western Kenya. Preliminary results, obtained from relating soil properties to topographic and climatic SCORPAN factors using random forests revealed differing importance of climatic and topographic predictors for different soil properties, suggesting different drivers behind variation in these properties. The accuracy of the predictions, measured with the root mean square error, was 0.96% for soil organic carbon, 0.31 for pH, 0.097 mg/kg for nitrogen, 9.77% for sand, 3.65% for silt, and 10.56% for clay. Maps for plantations with no validation data available were then sense-checked against the predictive soil maps for Africa (AfSIS).Further improvements in accuracies are expected from inclusion of NDVI image composites to aid soil carbon modelling as well as data on fertiliser applications within tea plots for nitrogen predictions, coupled with the XGBoost algorithm. Finalised maps are expected to be used within the digital platform for tea crop management called “Internet of TeaTM” or “IoTeaTM” that incorporates a model of tea growth and development called “CUPPA-Tea”. Alongside, the underpinning soil data will help us understand the fundamental processes in the soil that influence greenhouse gas emissions, and using advanced genomic technologies to accelerate the tea breeding process.