Condition-based maintenance (CBM) is emerging in the airline industry as a revolutionary concept that could potentially increase the efficiency of aircraft operations. The adoption of CBM strategies is not yet widespread due to the stringent requirements imposed by aviation authorities and the fact that the prognostic and health management (PHM) technology is still in its infancy. Along with the developing technology, CBM requires a performance evaluation in terms of aircraft maintenance schedule optimization. This paper proposes a novel reinforcement learning model to solve the airline maintenance scheduling problem subject to prognostics uncertainty. A Deep Q-Learning model is trained to schedule A-check routine tasks. The approach preserves the dichotomy of the maintenance problem by scheduling both interval-based clusters of tasks and condition-based tasks monitored with synthetic prognostics. Maintenance, repair, and overhaul (MRO) data from a major European airline was used to construct a realistic simulation and a model tailored to their operations. We explore a wide range of scenarios with varied numbers of tasks scheduled with a CBM policy, as well as different magnitudes of uncertainty in order to enable a viable maintenance strategy. Compared to traditional maintenance policies, the results demonstrate that the implementation of CBM reduces the fleet ground time and improves the task's interval utilization when assessing the uncertainty involved in prognostics.