  Asteroseismology is a powerful tool to probe the structure of stars.
Space-borne instruments like CoRoT, Kepler and TESS have observed the
oscillations of numerous stars, among which {\delta} Scutis are particularly
interesting owing to their fast rotation rates and complex pulsation
mechanisms. In this work, we inferred model-dependent masses, metallicities and
ages of 60 {\delta} Scuti stars from their photometric, spectroscopic and
asteroseismic observations using least-squares minimization. These statistics
have the potential to explain why only a tiny fraction of {\delta} Sct stars
pulsate in a very clean manner. We find most of these stars with masses around
1.6 {M_\odot} and metallicities below Z = 0.010. We observed a bimodality in
age for these stars, with more than half the sample younger than 30 Myr, while
the remaining ones were inferred to be older, i.e., hundreds of Myrs. This work
emphasizes the importance of the large-frequency separation ({\Delta\nu}) in
studies of {\delta} Scuti stars. We also designed three machine learning (ML)
models that hold the potential for inferring these parameters at lower
computational cost and much more rapidly. These models further revealed that
constraining dipole modes can help in significantly improving age estimation
and that radial modes succinctly encode information pertaining to stellar
luminosity and temperature. Using the ML models, we also gained qualitative
insight into the importance of stellar observables in estimating mass,
metallicity, and age. The effective surface temperature T eff strongly affects
the inference of all structure parameters and the asteroseismic offset
parameter {\epsilon} plays an essential role in the inference of age.
