  Surface-to-Air Missiles (SAMs) are crucial in modern air defense systems. A
critical aspect of their effectiveness is the Engagement Zone (EZ), the spatial
region within which a SAM can effectively engage and neutralize a target.
Notably, the EZ is intrinsically related to the missile's maximum range; it
defines the furthest distance at which a missile can intercept a target. The
accurate computation of this EZ is essential but challenging due to the dynamic
and complex factors involved, which often lead to high computational costs and
extended processing times when using conventional simulation methods. In light
of these challenges, our study investigates the potential of machine learning
techniques, proposing an approach that integrates machine learning with a
custom-designed simulation tool to train supervised algorithms. We leverage a
comprehensive dataset of pre-computed SAM EZ simulations, enabling our model to
accurately predict the SAM EZ for new input parameters. It accelerates SAM EZ
simulations, enhances air defense strategic planning, and provides real-time
insights, improving SAM system performance. The study also includes a
comparative analysis of machine learning algorithms, illuminating their
capabilities and performance metrics and suggesting areas for future research,
highlighting the transformative potential of machine learning in SAM EZ
simulations.
