Accurate prediction of aircraft turnaround time (TAT) is essential for mitigating reactionary delay, yet present methods remain constrained. Existent work uses discrete event simulations to predict individual ground activities but accumulate error and uncertainty, and in turn, other data-driven studies still provide a single static point estimate that is not updated with the latest operational information. It is therefore difficult to validate the use of these models in real operational environments. Although rolling, continuously updated forecasts have recently been explored for departure delay prediction, no study has yet extended such dynamic modelling to the turnaround itself, leaving a critical gap in operational decision support, as departure delay may include additional delay sources external to the turnaround process. This study develops and operationally tests a probabilistic machine learning framework that continuously updates full predictive distributions of TAT for a European hub carrier. Real time airline, meteorological and air‑traffic feeds are merged into gradient boosting tree ensembles trained with quantile regression. Evaluation on past turnarounds yields a median absolute error of 10 minutes immediately after the preceding take‑off, falling to 8 minutes at on‑block. Results show that the uncertainty of the prediction reduces by a quarter as updated operational data like delays or air-traffic control slots come in. These findings show that uncertainty in TAT can be quantified accurately and in near real-time using data streams already present in airline operations, enabling controllers and optimisation engines to target mitigation measures proportionately and thereby reducing cascading delay, cost, and emissions.