Modern manufacturing systems operate in increasingly dynamic and uncertain environments, making intelligent and adaptive decision-making essential for effective production control. One of the key challenges in such systems is dynamic work order prioritization within multi-constrained settings, where fluctuating demand, shared resources, machine breakdowns, workforce limitations, and conflicting performance objectives must be managed simultaneously. Traditional dispatching rules such as First-Come-First-Serve (FCFS) and Shortest Processing Time (SPT) are often inadequate, as they focus on single performance metrics and overlook broader operational and strategic factors. To address these limitations, this study presents a structured multi-criteria decision-making (MCDM) framework for dynamic work order prioritization in an electronic manufacturing context. The proposed framework integrates the Analytic Hierarchy Process (AHP) to determine the relative importance of decision criteria and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank competing work orders. Orders are evaluated based on multiple factors, including due date urgency, processing time, setup requirements, customer priority, profit contribution, resource availability, and work-in-process levels. A dynamic updating mechanism continuously recalculates priority scores as system conditions evolve, enabling real-time responsiveness to disruptions and demand variability. The framework was implemented and validated using a simulated multi-product electronic manufacturing system with capacity constraints and stochastic events, demonstrating improved on-time delivery, reduced average flow time, better machine utilization, and more balanced workload distribution compared to conventional single-rule dispatching approaches, thereby offering a scalable and effective decision-support solution for complex manufacturing environments.