This paper depicts the implementation of deep neural networks in predicting common faults of the turbojet engine bearings by training the model with images and processing them by designing proper Deep Neural Network model apart from conventional vibration analysis methods, for faster detection of bearing health and reusability. The turbojet engines have higher main-shaft speeds operating at elevated temperature conditions, reducing the bearing estimated life and thus the need of schedule maintenance. This system can identify some of the bearing damages like cracks, dents, fatigue, fretting and smearing conditions prevailing due to thermal effects, high axial and radial loads over the main-shaft, propeller shank and auxiliary systems bearings. It finally assists the aircraft maintenance engineers and technicians to reach to the conclusions of bearing conditions by taking pictures of bearings from any device and fetching them to the system for better results of bearing conditions.