Recent advances in artificial intelligence (AI) indicate that this technology will play a pivotal role in enhancing the capabilities of electronic warfare (EW) systems. Intelligent algorithms can significantly improve radar signal processing, emitter identification and classification, jamming detection, and the development of effective electronic counter-countermeasure strategies. In this paper, a Multilayer Perceptron (MLP) neural network is employed to develop a method for the classification and encoding of threat signals, as well as noise and deceptive radar jamming techniques. Owing to the nonlinear nature of the data, a feedforward neural network trained using the backpropagation algorithm was adopted. Simulation results obtained in the MATLAB environment demonstrate that the MLP network trained with the traingda algorithm achieved a classification accuracy of 99.8%, a specificity of 94.5%, and a sensitivity of 89.7% for deceptive jamming techniques. For noise jamming techniques, the proposed model attained a classification accuracy of 99.0%, a sensitivity of 91.86%, and a specificity of 91.0%. These findings highlight the strong capability of the proposed MLP-based approach in enhancing the intelligence of electronic attack and deception systems against advanced radar systems.