Predicting the remaining useful life (RUL) of turbofan engines, which allows for proactive interventions and improves system reliability, is a crucial task in predictive maintenance. To address the challenge of accurately estimating the RUL of turbofan engines, this paper presents a novel deep learning method that makes use of Self-Attention Transformers. Using the C-MAPSS FD001 dataset, we trained a multi-block Transformer encoder and showed that it could successfully capture long-range dependencies in sensor data. Through rigorous training, both training and validation losses effectively converge, demonstrating the model’s stable learning curves and its robustness in capturing data patterns. The model demonstrates high accuracy for lower RUL values, which is crucial for timely maintenance planning, although it shows some variance in predictions for higher RUL values. The thorough examination of the model’s interpretability is one of this work’s main contributions. We demonstrate how the model distributes its attention across the input sequence by extracting and visualizing the attention weights, with distinct attention heads focusing on patterns in recent or historical data. In addition to validating the model’s decision-making process, this analysis facilitates the development of predictive maintenance systems with enhanced transparency and reliability.