Paper Title
A Comprehensive Review on Computational Intelligence-Based Approaches for Inventory Management

Abstract
Supply chain management depends on inventory control, which also depends on exact coordination and optimization for economy. Modern supply networks' complexity and dynamic character might challenge inventory control's ability. Artificial intelligence, machine learning, and evolutionary algorithms among computer science technologies might help to address these problems. The state-of-the-art computational intelligence technologies of inventory management are reviewed in this paper. For demand forecasting, stock level optimization, and holding cost reduction it investigates neural networks, genetic algorithms, fuzzy logic, and swarm intelligence. Based on accuracy, processing economy, scalability, and real-time data application, the study contrasts approaches. These technologies can raise decision-making, inventory autonomy, and forecast accuracy. The study also combines computational intelligence approaches with contemporary technologies such as the Internet of Things (IoT) and blockchain, which improve inventory control by means of real-time data and safe transaction records. This thorough assessment of artificial intelligence used in inventory control exposes trends, weaknesses, and future directions of research focus. It underlines how these approaches may make supply networks more successful, flexible, and strong. The results should guide experts and scholars on the most recent developments and motivate creative ideas to solve inventory control challenges. Keywords - Inventory Control, Computational Intelligence, Demand Forecasting, Machine Learning.