In this study, we analyzed and compared two innovative proposed models, DBN-ELM and PSO-DBN, designed for optimizing Ultra-Wideband (UWB) antennas. The DBN-ELM model combines a Deep Belief Network (DBN) with an Extreme Learning Machine (ELM), significantly enhancing its ability to learn high-level features and accurately approximate nonlinear functions, which is crucial for optimizing antenna design. Conversely, the PSO-DBN model integrates Particle Swarm Optimization (PSO) to fine-tune the DBN structure, aiming to achieve the most effective antenna configurations. Both approaches have demonstrated outstanding performance in meeting design criteria, accurately fitting S-parameters, and reducing error rates compared to traditional methods. Our analysis indicates that these models not only improve predictive accuracy and generalization capabilities but also streamline the design process for complex UWB antenna systems. Additionally, we evaluated these models against a total of five models-DBN, DBN-ELM, PSO-DBN, MLP, and ANN-across the complex Minkowski notch rectangular antenna.