Aircraft propulsion health monitoring and prognostics are critical to ensuring operational reliability, safety, and cost-effectiveness. This study explores innovative methodologies for assessing the health status of turbofan engines, with an emphasis on F-16 aircraft propulsion systems. The proposed approach incorporates trending algorithms and advanced data analysis techniques to identify degradation patterns and predict engine failures before they occur. Key contributions include a comprehensive framework for engine performance data trending and novel algorithms for automatic data analysis, enabling accurate detection of anomalies and performance shifts. Utilizing engine monitoring system (EMS) data, including parameters like turbine temperatures, rotor speeds, and pressures, the study demonstrates methods to process and trend performance data. Various trending scenarios, such as scattered data, step changes, and parameter thresholds, are analyzed using statistical and algorithmic models. Case studies highlight the effectiveness of predictive tools like Long Term Slope (LTS), Three Point Average (TPA), and Predicted Value (PV) for timely maintenance actions. Proposed methodologies were verified and confirmed for the engine nozzle crunch failure. This research underlines the potential of incorporating artificial intelligence and neural networks into prognostic models, offering insights into remaining useful life estimation and diagnostics. By applying the presented methodologies, aircraft operators can enhance maintenance strategies, mitigate in-flight failures, and extend engine lifecycle. The findings contribute to advancing prognostic health monitoring systems for contemporary and future aircraft propulsion technologies.