In today's dynamic business environment, real-time data analytics has emerged as a transformative tool for enhancing supply chain efficiency. Traditional supply chain models often suffer from inefficiencies due to delays in data collection, analysis, and decision-making. Real-time data analytics leverages big data, artificial intelligence (AI), and the Internet of Things (IoT) to enable continuous monitoring, predictive insights, and agile decision-making. This paper explores the role of real-time data analytics in optimizing supply chain operations by improving demand forecasting, inventory management, transportation logistics, and risk mitigation. The integration of AI-driven predictive analytics enhances demand forecasting accuracy, allowing firms to optimize stock levels and reduce the risk of stockouts or overstocking. IoT-enabled sensors and RFID technology provide real-time tracking of goods, ensuring visibility across the entire supply chain. This real-time visibility minimizes disruptions, enhances supplier coordination, and improves customer satisfaction. Additionally, real-time analytics in transportation logistics enables dynamic route optimization, reducing delivery times and fuel consumption while enhancing overall efficiency. Machine learning algorithms play a crucial role in anomaly detection, identifying potential disruptions such as supplier delays, equipment failures, or demand fluctuations. By leveraging real-time analytics, organizations can implement proactive strategies, reducing the impact of uncertainties and improving resilience against supply chain disruptions. Furthermore, blockchain technology enhances data security and transparency, fostering trust among supply chain stakeholders. Despite its numerous advantages, the adoption of real-time data analytics in supply chains presents challenges, including data integration complexities, high implementation costs, and cybersecurity risks. Organizations must develop robust data governance frameworks and invest in scalable analytics platforms to maximize the benefits of real-time insights. This paper concludes that real-time data analytics significantly enhances supply chain efficiency by enabling data-driven decision-making, improving responsiveness, and optimizing resource utilization. Future research should focus on the integration of advanced AI models, edge computing, and blockchain technology to further enhance supply chain visibility, resilience, and sustainability.