  Nitric Oxide (NO) significantly impacts energy distribution and chemical
processes in the mesosphere and lower thermosphere (MLT). During geomagnetic
storms, a substantial influx of energy in the thermosphere leads to an increase
in NO infrared emissions. Accurately predicting the radiative flux of Nitric
Oxide is crucial for understanding the thermospheric energy budget,
particularly during extreme space weather events. With advancements in
computational techniques, machine learning (ML) has become a highly effective
tool for space weather forecasting. This effort becomes even more worthwhile
considering the availability of two decades of continuous NO infrared emissions
measurement by TIMED/SABER along with several other key thermospheric
variables. We present the scheme of development of an ML-based predictive model
for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have
been tested for better predictive ability, and an optimized model (NOEMLM) has
been developed for the study of NOIRF. This model is able to extract the
underlying relationships between the input features and effectively predict the
NOIRF. The NOEMLM predictions have very good agreements with SABER observation
during quiet time as well as geomagnetic storms. In comparison with the
existing TIEGCM model, NOEMLM has very good performance, especially during
extreme space weather conditions. The results of this study suggest that
utilizing geomagnetic and space weather indices with ML/AI can serve as
superior parameters for studying the upper atmosphere, as compared to focusing
on specific species having complex chemical processes and associated
uncertainties in constituents. ML techniques can effectively carry out the
analysis with greater ease than traditional chemical studies.
