Real-time data processing of the next generation of experiments conducted at FAIR requires a reliable reconstruction of event topologies and, therefore, will depend heavily on in-situ calibration procedures. A neural network-based approach can provide fast real-time calibrations based on continuously available environmental data: pressure, high voltage or beam intensity. We applied this approach to the data obtained from the Drift Chambers of HADES. To enhance regularization we incorporate information about previous environmental states into the Long Short-Term Memory (LSTM) architecture and combine it with Graph Convolutions to account for correlations between different chambers. Using our proposed prediction strategy we achieved stable and accurate predictions, matching the quality of an offline calibration. Moreover, our approach significantly reduces the calibration time, making it well-suited for real-time applications within high-rate data acquisition systems.