  The Extreme Universe Space Observatory on a Super Pressure Balloon 2
(EUSO-SPB2) is the most advanced balloon mission undertaken by the JEM-EUSO
collaboration. EUSO-SPB2 is built on the experience of previous stratosphere
missions, EUSO-Balloon and EUSO-SPB, and of the Mini-EUSO space mission
currently active onboard the International Space Station. EUSO- SPB2 is
equipped with two instruments: a fluorescence telescope aimed at registering
ultra-high energy cosmic rays (UHECRs) with an energy above 2 EeV and a
Cherenkov telescope built to measure direct Cherenkov emission from cosmic rays
with energies above 1 PeV. The EUSO-SPB2 mission will provide pioneering
observations on the path towards a space-based multi-messenger observatory. As
such, a special attention was paid to the development of triggers and other
software aimed at comprehensive data analysis. A whole number of methods based
on machine learning (ML) and neural networks was developed during the
construction of the experiment and a few others are under active development.
Here we provide a brief review of the ML-based methods already implemented in
the instrument and the ground software and report preliminary results on the
ML-based reconstruction of UHECR parameters for the fluorescence telescope.
