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Recent advancements of noninvasive imaging techniques applied for the study and conservation of paintings have driven a rapid development of cutting-edge computational methods. Macro x-ray fluorescence (MA-XRF), a well-established tool in this domain, generates complex and voluminous datasets that pose analytical challenges. To address this, we have incorporated machine learning strategies specifically designed for the analysis as they allow for identification of nontrivial dependencies and classification within these high-dimensional data, thereby promising comprehensive interrogation. We introduce a deep learning algorithm trained on a synthetic dataset that allows for fast and accurate analysis of the XRF spectra in MA-XRF datasets. This approach successfully overcomes the limitations commonly associated with traditional deconvolution methods. Applying this methodology to a painting by Raphael, we demonstrate that our model not only achieves superior accuracy in quantifying the fluorescence line intensities but also effectively eliminates the artifacts typically observed in elemental maps generated through conventional analysis methods.