The project involves developing a machine learning model to predict the remaining useful life (RUL) of a jet engine. Our approach leverages different types of recurrent neural networks and transformers to analyze historical data containing operational conditions from each engine's previous flight cycles. We aim to provide valuable insights for predictive maintenance in aviation, allowing engineers to also improve aircraft safety and optimize maintenance schedules.