Magnetic hysteresis, eddy currents, and manufacturing imperfections pose significant challenges for beam operation in multi-cycling synchrotrons. Addressing the dynamic dependency of magnetic fields on cycling history is a current limitation for control room tools using existing models. This paper outlines recent advancements to solve this, presenting the outcome of operational tests utilizing data-driven approaches and an overview of the next steps. Notably, artificial neural networks, including long short-term memory networks, transformers and other time series analysis architectures, are employed to model static and dynamic effects in the main dipole magnets of the CERN SPS. These networks capture hysteresis and eddy current decays based on measured magnetic field and data from the real-time magnetic measurement system of the SPS main dipoles. Cycle-by-cycle feed-forward corrections are implemented through the CERN accelerator controls infrastructure, which propagate corrections of magnetic fields to corresponding adjustments in the current of the power converters feeding the magnets.