  In today's ever-connected world, the demand for fast and widespread
connectivity is insatiable, making multibeam satellite systems an indispensable
pillar of modern telecommunications infrastructure. However, the evolving
communication landscape necessitates a high degree of adaptability. This
adaptability is particularly crucial for beamforming, as it enables the
adjustment of peak throughput and beamwidth to meet fluctuating traffic demands
by varying the beamwidth, side lobe level (SLL), and effective isotropic
radiated power (EIRP). This paper introduces an innovative approach rooted in
supervised learning to efficiently derive the requisite beamforming matrix,
aligning it with system requirements. Significantly reducing computation time,
this method is uniquely tailored for real-time adaptation, enhancing the
agility and responsiveness of satellite multibeam systems. Exploiting the power
of supervised learning, this research enables multibeam satellites to respond
quickly and intelligently to changing communication needs, ultimately ensuring
uninterrupted and optimized connectivity in a dynamic world.
