Machine learning (ML) is emerging as a key component of the CAS toolkit, connecting simulations and observations across astrochemistry. In our case, ML is not only employed to enhance computational performance, but also to improve the physical understanding of several astrophysically relevant environments by reducing chemical network complexity, analysing synthetic observations, mapping molecules in hot corinos and starless cores, and exploring planetary atmospheres.