Airline disruption management traditionally seeks to address three problem dimensions – aircraft scheduling, crew scheduling, and passenger scheduling – in that order. However, current efforts have, at most, only addressed the first two con-currently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another. Uncertainties in scheduling out-comes originate from random disruption events (like inclement weather and aircraft malfunction), the order in which they occur, and how they are resolved. As such, these uncertainties propagate through all problem dimensions of airline disruption management on day of operation. Existing approaches for airline operations recovery include human specialists who decide on the necessary corrective actions to airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information, necessary to make robust decisions that simultaneously address all three problem dimensions in operations recovery. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst the three dimensions in airline operations recovery, and provide objective insights to the specialists in the Airline Operations Control Center (AOCC).To this effect, this dissertation provides a discussion of an agnostic and systematic paradigm for enabling simultaneously-integrated recovery of all problem dimensions in airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology.