<p>This paper investigates the stability and synchronization properties of a class of delayed deterministic and stochastic neutral neural networks using Lyapunov-based techniques. Several appropriate Lyapunov functionals are constructed to derive new delay-dependent and delay-independent sufficient conditions for asymptotic, mean-square, and exponential mean-square stability. Compared with previously reported results, the obtained criteria are less conservative and provide wider admissible regions for the feedback gain, transmission delays, and stochastic perturbation intensity. The developed theoretical framework is further applied to synchronization-based neural communication systems with delayed feedback and stochastic disturbances. By considering a master–slave communication configuration, the derived stability conditions are interpreted as practical synchronization criteria guaranteeing robust signal transmission under communication delays and environmental noise. The obtained results provide explicit bounds on the allowable nonlinear feedback gain, admissible transmission delays, and stochastic perturbation levels while preserving synchronization stability. Several numerical examples and stability region illustrations are presented to demonstrate the effectiveness of the proposed approach and the improvement achieved over existing criteria.</p>