Empirical thresholds for landslide warning systems have benefitted from the incorporation of soil‐hydrologic monitoring data, but the mechanistic basis for their predictive capabilities is limited. Although physically based hydrologic models can accurately simulate changes in soil moisture and pore pressure that promote landslides, their utility is restricted by high computational costs and nonunique parameterization issues. We construct a deep learning model using soil moisture, pore pressure, and rainfall monitoring data acquired from landslide‐prone hillslopes in Oregon, USA, to predict the timing and magnitude of hydrologic response at multiple soil depths for 36‐hr intervals. We find that observation records as short as 6 months are sufficient for accurate predictions, and our model captures hydrologic response for high‐intensity rainfall events even when those storm types are excluded from model training. We conclude that machine learning can provide an accurate and computationally efficient alternative to empirical methods or physical modeling for landslide hazard warning. Plain Language Summary Rainfall‐triggered landsliding is widespread, damaging, and deadly. These landslides are also difficult to predict and require complex models to completely assess the risk of a single area. This is a time‐ and resource‐intensive process that makes it difficult to easily integrate into a system designed to give residents early warning of a potential landslide in their community. In an effort to work around these limitations, we introduce an alternative model based on artificial intelligence, which only requires a few minutes to develop and relies on easy‐to‐record information such as rainfall and soil moisture. We find this new model accurately forecasts essential information for establishing landslide risk and does so in significantly less time than similarly performing models. Therefore, when presented with the need to help inform community decision making, we encourage this type of modeling, given it takes little time to develop and produces accurate predictions of essential information for assessing community risk. Key Points We test the efficacy of using Deep Learning to forecast subsurface hydrologic response for hillslopes that are prone to shallow landsliding The model produces pore pressure predictions for landslide‐relevant conditions up to 36‐hr in advance with as little as 6 months of data Our work encourages further exploration of machine learning as part of the development of landslide warning systems