The pursuit of lightweight, structurally sound, and cost-effective designs is paramount in contemporary engineering, particularly in the development of advanced enclosed mast/sensor systems (AEM/S). A significant challenge in this field is the limited availability of comprehensive data and systematic classification of studies pertaining to these specific mast configurations. This research addresses this gap by initially developing a model of a steel and anti-radar composite mast, subsequently employing Abaqus/CFD and Abaqus/FEM to conduct aerodynamic and finite element analyses, respectively. This initial phase aims to characterize the structural behavior of this mast type. In the second stage, a neural network-based method, coupled with a genetic optimization algorithm, is implemented to determine the optimal dimensions for the mast and its associated casing. This optimization process culminated in a composite mast design, standing 22 meters tall with a 9.59-degree slope, achieving a substantial 50% weight reduction compared to the original steel mast design (reducing the mass from 118 to 27.51 tons). Finite element analysis (FEA) was utilized to assess the mechanical behavior of the initial and optimized designs. The results demonstrate that the optimized design exhibits a more evenly distributed stress profile, with reduced stress concentrations in the lower sections and increased stress levels in the upper antenna region. This stress redistribution suggests improved material utilization and enhanced structural integrity. By minimizing the potential for localized failure, the optimized design demonstrates the feasibility of achieving significant weight reductions without compromising structural performance. These findings underscore the effectiveness of artificial neural network (ANN)-based optimization in creating lightweight and efficient composite structures, providing valuable insights for designing AEM/S systems and other tall structures subjected to dynamic loads. The study highlights the potential of advanced optimization techniques and composite materials in achieving sustainable and high-performance engineering solutions.