This paper explores the feasibility of utilizing Deep Neural Networks (DNN) for analyzing terahertz (THz) antennas, specifically EBG patterned microstrip patch antennas. Conventional design methods face difficulties in complexity and time requirements as the demand for smaller antennas working in the THz band increases.To tackle this challenge, we explore the utilization of Deep Neural Network (DNN) models to accelerate the process of design and optimize antenna efficiency. Our results demonstrate the effectiveness of NN models in forecasting antenna behavior and optimizing performance. In our design, we achieved a return loss (S11) of -59 dB, a gain of 6.04 dB, and a voltage standing wave ratio (VSWR) of 1.02. By applying the DNN model to our design, We attained a remarkable precision of 98%, accompanied by a negligible error margin of merely 0.05%. Furthermore, we compared our NN model with other machine learning models and found that it outperforms them in terms of accuracy and reliability.The benefits of utilizing DNN models in THz antenna design are twofold. Firstly, they enable rapid design and optimization, reducing the time and complexity associated with conventional methods. Secondly, they facilitate improved performance optimization, leading to enhanced antenna performance. These advantages have significant implications for the development of THz communication and sensing technologies.