Thovhale, MulisaRananga, SEbrahim, RozeenaMasonta, Moshe T2026-06-302026-06-302026-05978-1-905824-76-2http://hdl.handle.net/10204/14833This paper presents a Machine Learning (ML) approach for predicting the Remaining Useful Life (RUL) of mobile phone devices to support circular economy strategies aimed at reducing e-waste. The study addresses the challenge of premature device disposal by developing a prediction model that estimates how long a device can continue functioning before reaching end of life. A synthetic dataset representing 1,000 devices operating over 1,460 days was generated using mathematical degradation equations, and four ML models were evaluated: Random Forest, Gradient Boosting, Support Vector Regression and a Long Short-Term Memory (LSTM) network. The LSTM achieved the best performance, with a Mean Absolute Error (MAE) of 153 days and an R² (coefficient of determination) of 0.47. The results show that RUL prediction can support repair, refurbishment and recycling decisions, enabling more sustainable device lifecycle management.FulltextenCircular economyE-waste managementLong Short-Term MemoryLSTMMachine LearningPredictive maintenanceRemaining useful LifeConference Presentation