Conformal automation allows for increased acceptability of automation tools in air traffic control. The key enabler for achieving conformity of automation tools in performing expert tasks, for example, air traffic conflict resolution, is the identification of ATCO preferences (conflict resolution strategies) and its ability to learn and recommend similar strategies. This research proposes a machine learning-based framework to learn and predict the air traffic conflict resolution strategies using an ensemble model of regressor and classifier chains. This framework enables the prediction and generation of a complete conflict resolution profile of the maneuvered aircraft. Similar and contrasting ATCO conflict resolution strategies are collected through human-in-the-loop experiments, using a real-time, high fidelity simulation environment, for model training and evaluation. The prediction results demonstrate that the ATCOs strategies encoded in the collected data can be learned by the model with high accuracy (95.1%, 93.7% for choice of aircraft) and low MAE( 0.38 Nm and 0.52 Nm for maneuver initiation distance) for the ATCOs' datasets. These results demonstrate high conformance of the model predicted maneuvered trajectories with the original ATCOs maneuvers.