To address the limited performance of existing chaotic systems in practical applications, this paper proposes a hyperchaotic system synchronization control method combining an interval type-2 fuzzy brain emotional learning controller (IT2FBELC) with a robust controller. The IT2FBELC approximates the unknown components of the hyperchaotic system, with its weights and parameters updated online via gradient descent to achieve synchronous tracking between the master and slave systems. The robust controller compensates for residual errors, driving the control output closer to the ideal value and further improving synchronization accuracy. Simulation results demonstrate that the proposed approach achieves high synchronization of hyperchaotic systems with superior tracking performance and computational efficiency compared to RBF neural networks, BP neural networks and conventional brain emotional learning models. Additionally, simulations for secure voice and image transmission confirm the method’s effectiveness and adaptability in confidential communication, providing theoretical support for practical applications of chaotic secure communication.