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In the last several years, laboratory automation and high‐throughput synthesis and characterization have come to the forefront of the research community. The large datasets require suitable machine learning techniques to analyze the data effectively and extract the properties of the system. Herein, the binary library of metal halide perovskite (MHP) microcrystals, MA_xFA_1−xPbI_3−xBr_x, is explored via low‐dimensional latent representations of composition‐ and time‐dependent photoluminescence (PL) spectra. The variational autoencoder (VAE) approach is used to discover the latent factors of variability in the system. The variability of the PL is predominantly controlled by compositional dependence of the bandgap. At the same time, secondary factor of variability includes the phase separation associated with the formation of the double peaks. To overcome the interpretability limitations of standard VAEs, the workflow based on the translationally invariant variational (tVAEs) and conditional autoencoders (cVAEs) is introduced. tVAE discovers known factors of variation within the data, for example, the (unknown) shift of the peak due to the bandgap variation. Conversely, cVAEs impose known factor of variation, in this case anticipated bandgap. Jointly, the tVAE and cVAE allow to disentangle the underlying mechanisms present within the data that bring a deeper meaning and understanding within MHP systems.