Abstract Accurately forecasting injection‐induced seismicity remains a major challenge that is critically important for energy production and storage as controlling mechanisms are complex and remain opaque. We clarify key processes by following time‐histories of bespoke parameters anticipated to control strength reduction (drawdown) through overpressure during fluid injection. Injection‐driven radial overpressure fields motivate key parameter groups representing magnitudes (r∂P∂r $rfrac{partial P}{partial r}$) and rates (t∂P∂t $tfrac{partial P}{partial t}$) of strength‐drawdown. We emphasize these physics‐informed features as inputs embedded in a two‐stage machine learning (ML) architecture applied to high‐resolution microseismic and injection data from the Utah FORGE and EGS Collab stimulation experiments. Permuted models, selectively including then excluding these physically‐inspired terms, predict temporal seismic evolution and capture trends and peaks with testable and discernible fidelity. Improved predictions result from including these diagnostic parameters—confirming that the derived physical parameters mechanistically mediate seismicity. The magnitude of strength drawdown (r∂P∂r $rfrac{partial P}{partial r}$) dictates the size and localization of seismic events during phases of steep spatial pressurization—particularly early in the injection phase near the well. Conversely, t∂P∂t $tfrac{partial P}{partial t}$ governs the timing and rate of onset of seismic activity during rapid injection transients, accounting for elapsed‐time effects. Our approach demonstrates the broader benefits of embedding physics‐guided features into ML frameworks toward mitigation of seismic hazard.