Predicting the Remaining Useful Life (RUL) of machinery and critical components is crucial for proactive maintenance and operational efficiency in industrial settings. This paper presents an approach to RUL prediction using the AdaBoost algorithm, a technique that iteratively improves prediction accuracy by focusing on difficult-to-predict cases. The AdaBoost algorithm will be extended to handle both binary and multi-class classification, enabling it to distinguish between various stages of degradation. By providing more granular insights into the health status of components, this approach enhances maintenance planning by allowing for more targeted, condition-based interventions. Early detection of varying levels of wear allows maintenance teams to schedule repairs or part replacements precisely when needed, reducing unplanned downtime and optimizing resource allocation. This study demonstrates the adaptability of AdaBoost in handling complex RUL prediction scenarios, thus supporting a more effective and data-driven approach to predictive maintenance in industrial applications.