Traffic congestion occurs as demand surpasses the available capacity of a road network, resulting to lower speeds and longer journey times; with route guidance constituting the primary control strategy to alleviate the problem. However, the effectiveness of route guidance is limited in high-demand conditions. In this work, we proposed a Model Predictive Control (MPC) framework that combines multi-regional route guidance with a novel demand management method. Route guidance is used to minimize the network's density imbalance while demand management is utilized to reduce the conditions that cause congestion. This can be achieved by manipulating vehicle routes (i.e., using route guidance) and/or by instructing a portion of the vehicles to wait at their origin before commencing their journey (demand management). Simulations are conducted to evaluate the performance of the proposed MPC optimization indicating the substantial improvements that can be achieved in traffic flow performance.