Few studies have utilized machine learning techniques to predict or understand the Madden-Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here we present a simple framework for real-time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures that predict a real-time MJO index using latitude-longitude maps of tropical variables. These ANNs make skillful MJO predictions out to ~17 days in October-March and ~10 days in April-September, and efficiently capture aspects of MJO predictability found in more complex, computationally-expensive models. Varying model input and applying ANN explainability techniques further reveal sources and regions important for ANN prediction skill. This simple machine learning framework can be more broadly adapted and applied to predict and understand other climate phenomena.