The adept management of stirred-tank blending systems is crucial for ensuring uniform product quality while keeping operational costs in check across the chemical, food, and pharmaceutical industries. Traditional decentralized Proportional–Integral–Derivative (PID) controllers, though widely used, often struggle with significant loop interactions, lack of coordinated control, and insufficient ability to handle process constraints, which can negatively impact both performance and efficiency. This study offers a comprehensive technical and economic comparison between conventional PID control and linear Model Predictive Control (MPC) applied to a 10 m³ continuous blending tank. The MPC strategy was designed with a 20-step prediction horizon, a 5-step control horizon, and a 30-second sampling interval, incorporating dynamic control objectives alongside an economic cost function aimed at optimizing raw material usage. Simulation results reveal that MPC outperforms PID significantly, achieving an impressive 88.2% reduction in integral absolute error, along with marked improvements in settling time and overshoot, leading to a more stable and predictable blending process. Economically, adopting MPC can result in a 5% reduction in raw material consumption, an increase in annual profits by around $0.48 million, and a payback period of less than two months. These findings highlight the dual advantages of MPC, offering enhanced process performance and considerable economic benefits, making a strong case for its implementation as a robust, reliable, and cost-effective control strategy in large-scale industrial blending operations where product quality and material efficiency are of utmost importance................. Keywords:.............PID control; Model Predictive Control (MPC); Stirred tank blender; Dynamic modeling; Economic evaluation; Process optimization.