This thesis primarily investigates the optimization of traffic flow and vehicular coordination in Cooperative Adaptive Cruise Control (CACC) systems under real-world uncertainties. Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, the introduction of communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. Existing solutions often expand the time headway based on upper-bound delay estimates, which inadvertently diminishes the system's traffic throughput. To address these issues, we introduce a decentralized Model Predictive Control (MPC) approach that incorporates Kalman Filters and state predictors to counteract the uncertainties posed by noise and communication delays.