GLEAM-AI is a neural surrogate of the Global Epidemic and Mobility model (GLEAM) — a stochastic, age-structured, metapopulation epidemic model in which local transmission within subpopulations is coupled to the movement of individuals between them. Running GLEAM at the scale required for calibration is computationally expensive, because each scenario is a large stochastic ensemble of simulations. GLEAM-AI eliminates this cost: it is a recurrent neural network that reproduces GLEAM's output for any scenario in the model's parameter space. Bands are the 50% and 95% Negative-Binomial prediction intervals over 300 draws. The spread is Negative-Binomial observation noise for a single scenario; inputs are hard-limited to the surrogate's training range.