Abstarct: Abstract This dissertation investigates and presents a novel event-triggered hierarchical consensus control strategy for stochastic nonlinear multi-agent systems (MASs) affected by uncertainties and external disturbances. The primary challenge addressed is achieving consensus among a group of agents with unknown nonlinear dynamics under the influence of stochastic noise and environmental perturbations. To reduce communication burden and enhance the efficient use of computational resources, an event-triggered mechanism is adopted, which significantly limits the frequency of control signal updates. To approximate the unknown nonlinear dynamics of the agents, a function approximation technique (FAT) baxsed on the Szasz–Mirakyan operator is employed as a universal and efficient estimator. Furthermore, to reduce computational complexity and facilitate controller design, a command filter structure is incorporated. The proposed distributed control strategy ensures semi-global uniform boundedness of all system signals while guaranteeing the desired control performance. The effectiveness of the proposed approach is validated through a comprehensive set of simulations and comparative scenarios under various conditions. The simulation results clearly confirm the efficiency, robustness, and superiority of the proposed method over conventional approaches, including fixed-threshold event-triggered control, neural network-baxsed estimators, and other benchmark methods. Moreover, to demonstrate practical applicability, simulation scenarios involving stochastic single-lixnk robotic systems are thoroughly analyzed. These findings substantiate the practical viability of the proposed approach for efficiently managing complex multi-agent systems in real-world settings.