Volume 2 - Issue 3, May - June 2026

Modern industrial operations face significant challenges due to unexpected equipment failures, leading to costly downtime and maintenance overheads. This paper explores the implementation of AI-driven predictive maintenance systems that utilize Machine Learning (ML) algorithms and Internet of Things (IoT) sensor data to forecast equipment degradation. By analyzing real-time parameters such as temperature, vibration, and pressure, the proposed system identifies anomalies before actual failures occur. We evaluate various ML models, including Random Forest and Long Short-Term Memory (LSTM) networks, to predict the Remaining Useful Life (RUL) of industrial machinery. The results demonstrate a substantial reduction in unplanned downtime and maintenance costs, proving that AI-driven approaches significantly outperform traditional reactive and scheduled maintenance strategies.

Jaid Samir Patwegar, Prof.R.B.Mane,"AI-Driven Predictive Maintenance" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(967-971) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.