The increasing vulnerability of Ku and Ka band satellite links to sophisticated, intentional interference (jamming) poses a significant cyberdefense challenge to critical communications. Traditional mitigation systems, often static and with slow response times, lack the agility and intelligence to counter these dynamic threats, rendering them ineffective against sophisticated jamming patterns. This paper presents a novel, machine learning-driven cyberdefense architecture that utilizes distributed intelligence across space and ground segments for dynamic anti-jamming. The proposed system features a ground-based AI engine, which uses a hybrid deep learning model to classify interference types with over 93% accuracy, enabling the strategic selection of countermeasures. These countermeasures are executed in real-time by a versatile toolkit on the satellite, which includes adaptive filtering, adaptive modulation, space-time block coding (STBC), and adaptive beamforming. Simulation results validate the system’s effectiveness, demonstrating a significant Signal-to-Interference-plus-Noise Ratio (SINR) improvement of 12–15 dB under severe jamming conditions and maintaining a residual Bit Error Rate (BER) as low as \(10^{-6}\) . With rapid convergence times under 300 ms, the proposed architecture offers a robust, agile, and resilient solution to ensure the integrity of satellite communications against modern cyber threats.