Neutrinos can experience fast flavor conversions (FFCs) in highly dense astrophysical environments, such as core-collapse supernovae and neutron star mergers, potentially affecting energy transport and other processes. The simulation of fast flavor conversions under realistic astrophysical conditions requires substantial computational resources and involves significant analytical challenges. While machine learning methods like Multilayer Perceptrons have been used to accurately predict the asymptotic outcomes of FFCs, their ‘black-box’ nature limits the extraction of direct physical insight. To mitigate this limitation, we employ two distinct interpretable machine learning frameworks—Kolmogorov-Arnold Networks (KANs) and Sparse Identification of Nonlinear Dynamics (SINDy)—to learn interpretable surrogates for the asymptotic input–output mapping from a FFC simulation dataset. Our analysis reveals a fundamental trade-off between predictive accuracy and model simplicity. The KANs demonstrates high fidelity in reconstructing post-conversion neutrino energy spectra, achieving accuracies of up to 90%. In contrast, SINDy yields a low rank, compact closed-form approximation of the input–output mapping, at the expense of some predictive accuracy. Critically, using these structured and sparse surrogates as diagnostic tools, we identify that the system’s evolution is most sensitive to the initial number density of heavy-lepton neutrinos when FCCs are triggered, compared to other physical quantities. Ultimately, this work provides a methodological framework for interpretable machine learning that supports genuine data-driven scientific discovery in astronomy and astrophysics, going beyond prediction alone.