Aircraft performance, certification and safety hinge on the precise analysis of flight maneuvers, necessitating a methodical approach to extract critical insights from flight data. This research outlines a systematic methodology that combines signal processing with machine learning techniques for the detection and analysis of aircraft maneuvers. The core of this methodology involves the Wavelet Transform, which meticulously unveils temporal intricacies within flight data, shedding light on pivotal time-frequency attributes crucial for aviation safety assessments. Augmenting this approach, Long Short-Term Memory (LSTM) models are employed to capture intricate temporal dependencies, extending the capability beyond that of standalone machine learning. This methodology not only enhances aviation safety but also finds wide-ranging applications. By examining flight attitudes during actions and extracting multi-parameter time histories, it establishes standardized time histories for each maneuver type, which are performed for system identification, air-data calibration, and performance analysis. This standardized technique significantly reduces the time needed for data pre-processing, enabling analysts to focus on in-depth analysis. The interdisciplinary collaboration underlying this research highlights the immense potential of combining signal processing and machine learning to shape the future of aviation research and applications, for example. It provides a versatile framework to analyze flight data and glean insights into pilot maneuvering, which can be instrumental in enhancing aviation safety, pilot training, and decision-making processes. This approach transcends the limits of conventional maneuver detection and analysis, laying the foundation for more precise and efficient flight operations. Its implications extend to various sectors of aviation research, emphasizing the pivotal role of integrated methodologies in shaping the trajectory of aviation safety and performance.