In the era of Industry 4.0, smart manufacturing systems demand high reliability, reduced downtime, and cost-effective operations. Traditional maintenance strategies, such as reactive and preventive maintenance, often lead to inefficiencies and unplanned breakdowns. This research explores the integration of Artificial Intelligence (AI) techniques—such as machine learning, deep learning, and data analytics—for predictive maintenance in smart manufacturing environments. The study highlights how real-time sensor data and historical equipment records can be leveraged to forecast failures before they occur. A framework is proposed using supervised learning models, including decision trees and neural networks, to predict health equipment and schedule timely interventions. The results indicate a significant improvement in operational uptime, maintenance cost reduction, and overall equipment effectiveness (OEE). This paper concludes that AI-driven predictive maintenance plays a crucial role in transforming traditional manufacturing systems into intelligent, self-monitoring infrastructures.