We present an artificial neural network (ANN) model that reconstructs > 30 keV electron flux measurements near the geomagnetic equator from low-Earth-orbit (LEO) observations, exploiting the global coherent nature of the high-energy trapped electrons that constitute the radiation belts. To provide training data, we analyze magnetic conjunctions between one of National Oceanic and Atmospheric Administration’s (NOAA’s) Polar Orbiting Environmental Satellites (POES) and National Aeronautics and Space Administration’s (NASA’s) Van Allen Probes. These conjunctions occur when the satellites are connected along the same magnetic field line and allow for a direct comparison of satellites’ electron flux measurements for one integral energy channel, > 30 keV and over 76,000 such conjunctions have been identified. For each conjunction, we fit the equatorial pitch angle distribution (PAD) parameterized by the function \(J_D=\ C\cdot\sin^N\alpha\). The resulting conjunction dataset contains the POES electron flux measurements, L and MLT coordinates, geomagnetic activity AE index, and C and N coefficients from the PAD fit for each conjunction. We test combinations of input variables from the conjunction dataset and achieve the best model performance when we use all the input variables during training. We present our model’s prediction for the out-of-sample data that agrees well with observations, giving R² > 0.70. We demonstrate the ability to nowcast and reconstruct equatorial electron flux measurements from LEO without the need for an in-situ equatorial satellite. The model can be expanded to include existing LEO data and has the potential to be used as a basis of future real-time radiation-belt monitoring LEO constellations.