Abstract: This paper proposes an optimal event-triggered control method based on prescribed performance constraints for nonlinear zero-sum game systems with unknown dynamics, achieving prescribed performance control under reduced computational resources. First, by introducing a prescribed performance function, the constrained system control problem is transformed into an equivalent unconstrained optimal control problem. Second, leveraging an off-policy data iteration mechanism, combined with polynomial fitting techniques and the Integral Reinforcement Learning (IRL) method, a model-free iterative control strategy is derived within a purely data-driven control law framework, addressing the challenges posed by unknown system dynamics. Furthermore, an event-triggered mechanism based on game theory is designed, effectively reducing sampling frequency and computational burden by modeling event-triggered sampling errors and control strategies as a zero-sum game. Then, a single critic neural network is employed to approximate the optimal control solution, with the designed strategy used to update the network weights. Finally, theoretical analysis and simulation results demonstrate the convergence and effectiveness of the proposed method.