Evaluating lubricant life is crucial for maintaining equipment reliability and preventing failures. Conventional methods often depend on original equipment manufacturer recommendations for lubricant changes, which may result in the premature disposal of operationally effective lubricants, leading to economic costs and degrading overall efficiency. The chemical properties of these samples are evaluated by calculating multiple parameters such as total acid number, total base number, oxidation index, soot level, and water contamination. In addition, rheological properties through viscosity index analysis and the tribological properties via friction-wear analysis are determined. In this study, an artificial neural network (ANN) (a four-layer perceptron) and an adaptive neuro-fuzzy inference system (ANFIS) are applied to predict oil conditions based on multiple calculated parameters. Performance and comparison of these advanced mathematical models are evaluated using statistical indices. Overall, the artificial intelligence (AI)-powered approach proved effective in predicting lubricant life for HEMM. Among the AI models, the ANN model demonstrated particularly strong performance, with a correlation coefficient of 0.99 compared to 0.98 for the ANFIS model. Implementing the ANN model could lead to a potential 19% reduction in current engine oil expenses, which would lower operating costs and decrease environmental impact by reducing the frequency of oil disposal.