One of the major health challenges that humans have been facing for the last few decades is antimicrobial resistance (AMR), where antibiotics stop responding to infections and, thereby, take a considerable amount of time to cure them while increasing mortality rates. There are various steps that have been taken by researchers and organizations to identify, cure, and prevent this urgent issue. Here, in this review, we have tried to illustrate how machine learning has been used with different spectroscopic analytical platforms—Raman spectroscopy (RS), Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR)—to further accelerate the understanding and early detection of AMR. The combination of ML algorithms with advanced spectroscopic techniques delivers faster and deeper insights into the different mechanisms of bacterial resistance, delivering novel solutions.