From a wide variety of sources, including aircraft sensors, flight data recorders, maintenance systems, air traffic control networks, meteorological services, and passenger service platforms, the aviation industry produces enormous amounts of both organised and unstructured data. The sector, which operates in a highly complex and safety-critical environment, depends more and more on cutting-edge data-driven solutions to improve performance and guarantee dependability. In order to make airline operations more effective, secure, and sustainable, this study looks at the significance of predictive modelling and big data analytics (BDA). As global aviation traffic continues to increase due to globalisation and growing passenger demand, the need for intelligent, data-driven decision-making has become essential for maintaining competitiveness and regulatory compliance. In order to extract useful insights from high-velocity and high-volume datasets, the study investigates how airlines, airports, and air navigation service providers use sophisticated analytics and machine learning approaches. The efficacy of several predictive techniques, such as regression models, decision trees, ensemble learning techniques, and neural networks, in predicting maintenance needs, optimising fuel consumption, anticipating flight delays, controlling passenger demand, and improving the workforce scheduling, is examined. Particular focus is placed on predictive maintenance, which makes use of real-time aircraft health monitoring data to detect potential malfunctions, minimise operational disruptions, and enhance general safety. The study also emphasises the advantages of analytics-driven optimisation for the economy and environment. By enhancing route planning, load balancing, and fuel efficiency—all of which reduce carbon emissions—predictive systems help save costs and promote sustainability. Scalable, real-time analytics across aviation networks are made possible in large part by the integration of big data platforms with cloud computing and Internet of Things (IoT) technology. Notwithstanding these benefits, the report points out a number of drawbacks, such as data silos, cybersecurity threats, legal restrictions, problems with model interpretability, and expensive infrastructure. To overcome these constraints, methods like the use of explainable AI, standardized data architectures, and strong governance frameworks are examined. The findings conclude that Big Data Analytics and predictive modeling are critical drivers of innovation, resilience, and long-term growth in the aviation industry.