Border delays, travel restrictions, and shifting customer preferences all had an enormous effect on full-service and low-cost airlines during the COVID-19 crisis, which caused a spectacular halt in international air transport. Airways faced severe economic difficulties, disruptive operations, and a sharp decrease in passenger volume. In order to explain how company models, efficiency in operation, and data-driven choice making contributed to the aviation sector's durability and growth, this article uses the analysis of big data to examine the major airlines' post-pandemic recovery.

The project employs a numerical and comparison approach, utilising secondary data from 2016 to 2024. The passenger turnaround index, load factor, and the operational capacity of airlines, including full-service and low-cost carriers Air India, IndiGo, Singapore Airlines, and Scoot, were among the performance parameters assessed through the use of big-data analysis techniques. In order to forecast the number of clients for the next years 2025 to 2030, the Random Forest algorithm was used. To determine their precision and dependability in forecasting future trends, the models were tested using statistical indicators including R-squared and accuracy.

After a first modest rebound, full-service carriers have seen long-term growth as foreign demand has started to return. The findings support the need of analytics based on big data for forecasting demand, operational efficiency improvements, and tactical preparation during recovery.