Aviation is widely recognised as a system of systems where interconnected components interact dynamically within a structured framework. Failures in aviation equipment, inconsistencies in technological procedures, and operational inefficiencies contribute to stochastic variability, making robust data-driven approaches essential for enhancing sustainability and resilience. This study proposes a comprehensive statistical data processing framework aimed at enhancing the sustainability and resilience of civil aviation systems, using Ukraine as a case study. Our analysis identifies two major gaps: an insufficient application of modern data processing techniques and a lack of consideration for the changepoint effect—a critical factor influencing reliability indicators, diagnostic parameters, and technological process trends. The scientific novelty and value of this article lie in the development of a new approach to data processing in civil aviation, which includes a set of methods for changepoint detection, the estimation of the model parameters after the changepoint, and the prediction of future values in trends of processed data. The practical value is associated with the possibility of implementing such processing for all components of civil aviation, where process parameters and trends of diagnostic variables for components of civil aviation systems are monitored. The analysis of the efficiency of the proposed approach to data processing showed the possibility of reducing operating costs, which can be considered within the framework of sustainable development of civil aviation. An important practical result is that the authors propose a Datahub model to facilitate the efficient collection, processing, and usage of aviation-related statistical data, supporting both sustainable decision-making and cost minimisation. A case study on aviation radio equipment demonstrates the application of statistical data processing techniques, incorporating the changepoint effect through Monte Carlo simulations.