Despite the continuous advances in the fields of control and computing, the design and deployment of an efficient Large-scale Nonlinear Traffic Control System (LNTCS) remains a significant objective. This is mainly due to the complexity and strong nonlinearities involved in the modeling of traffic flow processes. The ultimate performance of a designed or operational LNTCS depends on two main factors: (a) the exogenous influences, and (b) the values of some design parameters included within the LNTCS. When a new control algorithm is implemented there is a period of, sometimes tedious, fine-tuning activity that is needed in order to elevate the control algorithm to its best achievable performance. Fine-tuning concerns the selection of appropriate values for a number of design parameters included in the control strategy. This thesis introduces and analyzes a new learning/adaptive algorithm that enables automatic fine-tuning of LNTCS, so as to reach the maximum performance that is achievable with the utilized control strategy. The proposed Adaptive Fine Tuning (AFT) algorithm is aiming at replacing the conventional manual optimization practice with a fully automated online procedure.