The integration of Artificial Intelligence (AI) into manufacturing industries is revolutionizing process control and efficiency. AI technologies, including machine learning, predictive analytics, and robotics, offer unprecedented capabilities to analyse vast datasets, predict outcomes, and optimize operations. This transformation addresses the growing demands for higher productivity, cost-effectiveness, and sustainability in a competitive global market. By replacing traditional rule-based systems with intelligent algorithms, manufacturing processes can adapt dynamically to changing conditions, reduce variability, and enhance quality. AI-driven solutions significantly improve efficiency through predictive maintenance, real-time monitoring, and process optimization. For example, machine learning models identify patterns in equipment performance, enabling early detection of potential failures and minimizing downtime. Additionally, AI-powered automation enhances operational accuracy, reduces human error, and supports scalability. These advancements are particularly relevant in Industry 4.0, where interconnected systems generate continuous data streams for actionable insights. However, adopting AI in manufacturing is not without challenges. Barriers include high implementation costs, workforce reskilling requirements, and data integration complexities. Overcoming these obstacles demands a structured approach, including investment in AI infrastructure, robust data governance frameworks, and fostering a culture of innovation. This paper explores the transformative role of AI in manufacturing, focusing on its application in process control and efficiency improvement. By analysing case studies and technological advancements, it provides actionable insights for stakeholders aiming to leverage AI for sustainable growth and competitive advantage.