Predicting the remaining useful life (RUL) of turbofan engines with accuracy is a major challenge in predictive maintenance since it impacts cost-effectiveness, safety, and operating efficiency. This research compares multiple RUL prediction approaches applied to NASA’s turbofan engine dataset. Our study includes a wide range of methods from the supervised, unsupervised, and hybrid learning paradigms. These methods include Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), Principal Component Analysis (PCA), and Transformer models. The study rates these models based on their predicted accuracy, computational complexity, and real-world application. Each model is evaluated in terms of forecast accuracy (via RMSE and percentage error) and bias in “Predicted RUL” at 25 and 50 cycles before failure. The Transformer model consistently achieves the lowest RMSE and more accurate RUL estimates across all operating conditions, while GRU/LSTM show higher errors at early/intermediate cycles. We quantify accuracy using RMSE and define “Predicted RUL” as the model’s output (the estimated remaining cycles); deviations from the true RUL indicate prediction bias. The results reveal that self-attention models capture long-range dependencies more effectively, guiding the selection of robust predictive maintenance models and suggesting future research directions. These insights assist researchers and practitioners in choosing models for reliable aerospace engine prognostics.