The emergence of Industry 4.0 has transformed modern manufacturing into a network of intelligent, data-driven systems. Among the technologies driving this change, Artificial Intelligence (AI)-based Predictive Maintenance (PdM) has proven to be one of the most effective tools for enhancing equipment reliability and reducing production downtime. This research paper explores the real-world application of AI-driven PdM in three major manufacturing organizations—General Motors, Siemens, and Bosch—each of which represents a distinct yet successful approach to implementing smart maintenance. General Motors deployed an AI and IoT-based anomaly detection system, reducing unplanned downtime by 60% and saving approximately $40 million annually. Siemens implemented Digital Twin technology integrated with AI, achieving a 30% reduction in maintenance costs and a 25% increase in operational efficiency. Bosch adopted a cloud-based AI maintenance platform, extending machine lifespan by 25% and cutting costs by 20%. By comparing these implementations, the study highlights how the integration of AI, IoT, and data analytics transforms tradi- tional maintenance into a proactive, efficient, and sustainable process. The findings reinforce Predictive Maintenance as a key enabler of smart manufacturing and industrial innovation in the era of Industry 4.0.