In order to create reliable models for precisely estimating the remaining usable life (RUL) of turbofan engines—a system that requires both complexity and safety—this research makes use of machine learning. To predict the RUL of industrial machines, the study focuses on building and assessing a machine-learning model. It investigates several techniques, including Random Forest, k-Nearest Neighbors (kNN), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). By utilizing these methods, the aviation sector may create sophisticated predictive maintenance solutions that maximize operational effectiveness, lower costs, and increase safety.