Spectral analysis has established methods that are exact and won't benefit from ML. As far as I understand the field that studies approximated or constrained spectral analysis is compressed sensing : that might have overlaps with ML.

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

The NHITS model enhances the multi-step forecasting strategy by incorporating innovative hierarchical interpolation and multi-rate data sampling techniques inspired by wavelet analysis.

It assembles its predictions sequentially and emphasizes its components with different frequencies and scales. NHITS significantly improves accuracy in long-horizon forecasting tasks while reducing computation time by orders of magnitude compared to existing neural forecasting approaches.

There are some chemicals and mixtures whose bands could overlap and make it difficult to distinguish between active sites (IE multiple O-H bonds etc.). Still agree that ML with spectral analysis is unnecessary, but that is a possible niche it could have.

I might be off base here but my first thought was there might be something there with integrating the full area of peaks and sorting out peaks from specific elements in the spectral analysis of a heterogeneous mixture (possibly through a Fourier transform or convolution? This is ringing bells for me as feeling similar to signal processing…)

It really depends on what you’re trying to learn

I have seen image classification networks being used to classify sounds via spectrograms. It is perfectly conceivable to use ML to analyze spectrograms or to manipulate them and turn them back into sounds. Of course you can also do that directly by using time-series models.

But as long as you have a problem that can be modeled mathematically, you are usually better off to stick to mathematical models. They are usually more computationally efficient and predictable.

I was thinking the same way, but then came to me the idea of, once I have detected peaks in a spectrum, I could distinguish peaks of different nature (eg. gaussian vs lorentzian) knowing the peak symmetry, the FWMH, or similar characteristics. I wouldn't be able to quantify the elements but i could use ML at a certain point, i guess.