A data-driven approach for task allocation can significantly improve sewing line efficiency by ensuring that the workload is evenly distributed among operators based on their individual skill level and speed, thereby reducing non-productive time and optimizing machine utilization. This balanced allocation minimizes the accumulation of work-in-progress (WIP) at specific stations. Our approach is to develop an optimization model that dynamically assigns tasks to operators, with the objective of minimizing total line completion time and maximizing line efficiency. Our methodology involves collecting real-time performance data (e.g., individual operator efficiency, task processing times) and feeding it into a Heuristic Optimization Algorithm (e.g., based on queueing theory and operator skill matrices). This algorithm dynamically recommends the optimal task assignment to balance the line. Improvement after using this approach is expected to be a significant increase in line efficiency (e.g., 10-15%) and a reduction in production lead time. The implication of this approach is a paradigm shift towards a smart, flexible manufacturing system in the Bangladeshi garment industry, enabling faster response to market demands and enhancing global competitiveness through better resource utilization.