This paper presents an analysis of advanced deep learning techniques for determining the remaining useful life (RUL) of aircraft engine components, with a focus on turbofans and high-pressure compressors. Through this analysis the importance of RUL forecasting to ensure flight safety and efficiency has been emphasized. The study includes papers that use the C-MAPSS Aircraft Engine Simulator Data provided by NASA, which allows realistic simulation of engine damage over different periods of time. Four different datasets were simulated under different operating environments and fault mechanisms, related to engine health with multiple sensors to effectively predict the RUL of turbofan jet engines. Different learning techniques, including convolutional neural networks (CNN), long short-term memory (LSTM) networks, autoencoders, recurrent neural networks (RNN), gated recurrent units (GRU), together with sparse autoencoders were investigated and their applications in RUL prediction with respect to capabilities and limitations were examined. Furthermore, the paper provides an extensive analysis of these salient deep learning models, using the root mean squared error (RMSE) as an evaluation performance metric. This study provides extensive insights into the effectiveness of these methods in RUL prediction, and facilitates informed decisions for practical applications.