Demand forecasting and supply chain efficiency issues in the metallurgical sector generate production inefficiencies and market instability. Traditional forecasting techniques fail to adapt to changing demand, causing overproduction or material shortages. AI-driven predictive analytics utilizing ML, DL, and big data analytics has revolutionized metallurgical marketing decision-making. This study analyzes historical sales data, market trends, and consumer behavior to optimize demand forecasting using AI and predictive modeling. AI approaches including time series forecasting, neural networks, and regression models being tested for materials demand prediction. Our AI-driven supply chain optimization article focuses on inventory management, logistics, and supplier relationship management. A comparison shows that AI-enhanced models provide real-time data processing, automation, and superior accuracy than traditional forecasting methods. Research on AI in metalworking has demonstrated promising outcomes in cost reduction, risk mitigation, and operational agility. Research challenges include ethics, implementation costs, and data quality. AI-driven decision-making has drawbacks. Finally, we consider blockchain integration, AI-driven sustainable manufacturing, and human-AI interaction in Industry 5.0. AI-powered predictive analytics may improve metallurgical supply chain planning and marketing, making them more efficient and competitive.