This Thesis focuses on synthesis & application of robust synchronization for complex dynamical networks with delay and uncertainty. First of all, the approach is based on ordinary differential equation (ODE) dynamical network model. Using guaranteed cost control scheme, the guaranteed cost synchronization of complex networks with uncertainty and time-varying delay is investigated. In addition, the optimal guaranteed cost synchronization problem for a class of coupled neural networks with Markovian jump parameters and mode-dependent mixed time-delay is investigated by employing a new Lyapunov-Krasovskii functional and some analytic skills. The reaction-diffusion neural network is often described by semilinear diffusion partial differential equation (PDE). Secondly, the PDE dynamical network model is also considered here. The asymptotical synchronization and H∞ synchronization for coupled reaction-diffusion neural networks with mixed delays (that is, discrete and infinite distributed delays) and Dirichlet boundary condition is investigated. Finally, the results of synchronization for complex dynamical networks here be extended to consensus of multi-agent systems. The noise tolerance leader-following of high-order nonlinear dynamical multi-agent systems with switching topology and communication delay is explored via the common stochastic Lyapunov functional stability theory