This paper presents a compact dual-band dielectric resonator antenna (DRA) with circular polarization reconfigurability, optimized for UAV platforms, satellite communications, and emerging 6G networks. An elliptical ground slot enhances impedance bandwidth, while an asymmetric feeding structure excites orthogonal modes for robust circular polarization. The design achieves 111.1 % impedance bandwidth, dual axial ratio bandwidths of 60.4 % and 17.4 %, a peak gain of 12.8 dBic, and 95.07 % radiation efficiency. Alongside full-wave simulations, machine learning (ML) models—Artificial Neural Networks (ANN), XGBoost, and Random Forest—are employed to predict S11 and AR across 3–15 GHz. The models attain high accuracy (R2 > 0.93 for S11, R2 > 0.96 for AR) with low error values, validating their ability to capture complex electromagnetic behavior. This hybrid design and ML-assisted approach accelerate optimization, reduce simulation time, and provide reliable predictive capability. The proposed DRA’s combination of wideband performance, polarization agility, and ML integration makes it a strong candidate for compact, high-performance communication systems in UAVs, satellites, and next-generation wireless infrastructure.
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