In the evolving landscape of the automotive industry, lean manufacturing and Kanban systems play a critical role in optimizing production flows and reducing waste. However, traditional Kanban systems often struggle with limitations in handling demand variability, stockouts, and overproduction, especially in dynamic environments. This paper explores the integration of Artificial Intelligence (AI) tools to enhance the reliability of Kanban systems, ensuring higher responsiveness and accuracy in inventory management and production planning. The study focuses on how AI-driven demand forecasting, predictive analytics, and real-time data collection via IoT sensors can mitigate the key challenges of traditional Kanban systems in the automotive sector. Specifically, machine learning models for predictive demand forecasting are used to optimize inventory levels, while automated replenishment algorithms ensure timely and precise stock refills. Through the case study presented, we demonstrate significant improvements in production efficiency, reduction of waste, and better synchronization across supply chains. The results showcase that integrating AI technologies into Kanban systems not only enhances operational reliability but also aligns with lean manufacturing principles by minimizing waste and increasing production flexibility. This approach promises to redefine the role of Kanban in the future of smart automotive manufacturing.