The increasing complexity of global supply chains, coupled with rising customer expectations and market uncertainties, has accelerated the adoption of Artificial Intelligence (AI) technologies in supply chain management. AI-driven solutions enable organizations to improve forecasting accuracy, optimize inventory management, enhance transportation planning, mitigate risks, and increase operational efficiency. This paper examines AI-based supply chain optimization strategies and their impact on organizational performance. The study reviews contemporary AI applications, including machine learning, predictive analytics, natural language processing, computer vision, and autonomous systems, within various supply chain functions. Findings indicate that AI significantly improves demand forecasting, supplier management, warehouse operations, route optimization, and resilience against disruptions. Despite these benefits, challenges related to data quality, implementation costs, cybersecurity, workforce adaptation, and ethical considerations remain significant. Future developments integrating AI, Internet of Things (IoT), blockchain, and digital twins are expected to revolutionize supply chain ecosystems. The study concludes that AI-driven supply chain optimization represents a strategic imperative for organizations seeking competitiveness and sustainability in dynamic business environments.