In today's world, Principal Component Analysis (PCA) is crucial for simplifying complicated datasets to monitor airplane health in real-time. PCA identifies vital patterns in the intricate and vast datasets. Aircraft sensors create an excessive amount of data containing duplicate or interrelated information, thus, the PCA makes a difference by creating an orthogonal set of important components that capture the data's majority variation, enabling the system to concentrate on selective data streams. Such reduction is advantageous since it fasten up data processing, reduces computation complexity, and enhances the identification of anomalies by eliminating irrelevant patterns and noise. As a result, PCA ensures that airplane health assessment is efficient and accurate it aids in making maintenance choices promptly, thereby enhancing safety.