<![CDATA[High performance electron guns are fundamental components in linear accelerators (linacs), directly influencing beam quality and downstream system efficiency. However, designing electron guns for applications such as a 6 MeV linac presents complex trade-offs between current, perveance, and beam emittance. Traditional simulation-driven optimization methods are computationally expensive and limit rapid prototyping. In this study, we develop a neural network-based surrogate model trained on CST Studio Suite simulation data to predict the electron gun's performance metrics. Our approach significantly accelerates the optimization process by providing real-time predictions of beam current and perveance across a wide design parameter space. The surrogate model achieves high prediction accuracy, with training and validation losses on the order of 10⁻⁷. Results demonstrate that neural network models can serve as reliable and efficient tools for electron gun design, offering considerable computational savings while maintaining accuracy. Future extensions include expanding the surrogate model to multi-objective optimization and incorporating thermal and mechanical effects into the design process.]]>
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