Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/4047 2018 2018 Department of Computer Information Systems, College of Computer Science and Information Technology, University of Sumer, Dhi-qar, Iraq Raed Raed Department of Computer Science, College of Computer Science and Information Technology, University of Sumer, Dhi-qar, Iraq Hiyam Hatem The Fourth Industrial Revolution represents a shift to a more connected, digital world across all industries, including mining. The application of smart sensors will reduce site risks and fuel consumption, reduce equipment breakdowns, improve preventative maintenance, and improve equipment efficiency, including dump truck engines. Dump truck fuel efficiency is influenced by a number of real-world factors, including driver behavior, road and weather conditions, and vehicle specifications. Additionally, potential engine failures and other aspects can impact vehicle outcomes. By using dynamic on-road data to predict fuel consumption per trip, the industry can effectively minimize the expense associated with driving evaluations. Furthermore, analysis of data provides valuable insights into identifying the underlying causes of fuel consumption by analyzing input parameters. This paper proposes and evaluates novel models for predicting dump truck fuel consumption and engine failures in open-pit mining. These models combine the power of features derived from data collected locally by dump truck sensors and their analysis. The fuel consumption prediction architecture for open-top mining trucks using an improved Long Short-Term Memory (LSTM) model and a double-layer thick Deep Neural Network (DNN) forms the basis of the model design, which consists of two separate components. Multi-delay Recurrent Neural Network (RNN) models have been found to be efficient and accurate. The RNN architecture is applied to capture the cyclic components and complex rules in engine consumption data. This research relied on essential factors (route, vehicle speed, engine revolutions, and engine load). The proposed model outperforms existing models, achieving (MAE=0.0210), (RMSE=0.0294), (MSE=0.0009), and accuracy (R²=0.9842), demonstrating that it can produce highly accurate predictions. 2026 2026 56 69 10.54216/FPA.210204 https://www.americaspg.com/articleinfo/3/show/4047