  This study evaluates the efficacy of two machine learning (ML) techniques,
namely artificial neural networks (ANN) and gene expression programming (GEP)
that use data-driven modeling to predict wall pressure spectra (WPS) underneath
turbulent boundary layers. Different datasets of WPS from experiments and
high-fidelity numerical simulations covering a wide range of pressure gradients
and Reynolds numbers are considered. For both ML methods, an optimal
hyperparameter environment is identified that yields accurate predictions. ANN
is observed to be faster and more accurate than GEP with an order of magnitude
lower training time and logarithmic mean squared error ($lMSE$), despite a
higher memory consumption. Novel training schemes are devised to address the
shortcomings of GEP. These include (a) ANN-assisted GEP to reduce the noise in
the training data, (b) exploiting the low and high-frequency trends to guide
the GEP search, and (c) a stepped training strategy where the chromosomes are
first trained on the canonical datasets followed by the datasets with complex
features. When compared to the baseline scheme, these training strategies
accelerated convergence and resulted in models with superior accuracy ($\approx
30\%$ reduction in the median $lMSE$) and higher reliability ($\approx 75\%$
reduction in the spread of $lMSE$ in the interquartile range). The final GEP
models captured the complex trends of WPS across varying flow conditions and
pressure gradients, surpassing the accuracy of Goody's model.
