Gas turbine components are susceptible to degradation during operations; hence, the identification of the engine condition is really important for the gas turbine users. To this end, a comprehensive adaptive diagnostic tool is an important step to monitoring the engine health condition and planning appropriate maintenance actions, thereby increasing the availability and reliability of the unit, and at the same time reducing the operation and maintenance expenses. In this paper, the capability of PYTHIA; a computer software technology for engine diagnostic purpose using a non-linear gas path analysis was explored on GE MS7001EA industrial heavy duty gas turbine during a plot period of 12,000 hours. The method used in this paper was to adapt an accurate engine performance model from the real engine historical data readings, and by implicating multiple component degradation parameters onto the diagnostic tool; which represents the possible phenomena in the real engine operation period. The adaptive gas path analysis was used to identify the level of degradation or health indices of the gas turbine at the module level and its degraded performance compared with the actual engine data trending. The results obtained indicated the capability of PYTHIA to successfully adapt real engine data and detect fault patterns in response to implanted faults of selected measurement set during engine operation period. The deviations between the predicted and measured values showed a satisfactory result with a root mean square error (RMS) ≤ 0.004 and Gas Path Analysis index value ≥ 0.996. The component parameter degradation during the 12000 hours engine operation was detected, indicating a decrease in flow capacity by 2.1% for compressor and turbine by 2.8%.