To improve operational efficiency, adaptability, and sustainability in an intelligent factory within the Industry 4.0 era, decision-making must happen in real-time. This paper presents a framework that relies on Digital Twin-Driven Operational Management to IoT, AI-driven analytics, and edge computing for cost, time, data-driven and real-time decision-making in manufacturing environments. The framework utilizes real-time sensor data, predictive modeling, and optimization algorithms for enhanced allocation of resources, production scheduling and effective fault recognition. A case study conducted in a smart manufacturing facility validates the framework, resulting in remarkable improvements in operational efficiency, system adaptability, and cost reduction compared to traditional models. The results of the experiment are increased accuracy in decision-making, decreased downtime, and optimized energy consumption, establishing the framework as a feasible solution for next generation smart factories. This study helps provide a better balance between ever-growing theoretical advancements and industrial application of the theory, leading to more resilient, autonomous and data-driven manufacturing ecosystems.