This research paper aims to explore the application of artificial intelligence (AI) and machine learning (ML) techniques in inventory management to enhance efficiency and minimize costs. It investigates how AI and ML can revolutionize traditional inventory management practices by leveraging advanced algorithms and data-driven decision-making. The study builds upon the theoretical foundations of inventory management and combines it with the capabilities of AI and ML. It explores concepts such as demand forecasting, inventory optimization, stock replenishment, and supply chain optimization, and examines how AI and ML algorithms can be utilized to enhance these processes. This review paper employs a systematic approach to gather and analyze existing research studies, industry reports, and case studies related to the integration of AI and ML in inventory management. It synthesizes the findings and identifies the key trends, challenges, and opportunities associated with this technological integration. The research highlights the potential benefits of AI and ML in inventory management, including improved demand forecasting accuracy, optimized stock levels, reduced stockouts, enhanced customer satisfaction, and lower operational costs. Additionally, it discusses the challenges and ethical considerations associated with the adoption of AI and ML in this domain. The practical implications of the findings provide valuable insights for organizations seeking to leverage AI and ML technologies to enhance their inventory management practices. This research paper contributes to the existing literature by providing a comprehensive overview of the role of AI and ML in inventory management. It synthesizes the latest research and industry developments, offering a holistic understanding of the benefits, challenges, and opportunities that arise from implementing AI and ML solutions. The paper also emphasizes the need for further research in areas such as explainable AI, human-AI collaboration, and the ethical implications of AI-driven inventory management systems.