Many problems exist within the myriad of currently employed screening and diagnostic methods. Further, an incredibly wide variety of procedures are used to identify an even greater number of diseases which exist in the world. There is a definite unmet clinical need to improve diagnostic capabilities of these procedures, including improving test sensitivity and specificity, objectivity and definitiveness, and reducing cost and invasiveness of the test, with an interest in replacing multiple diagnostic methods with one powerful tool. There has been a recent surge in the literature which focuses on utilizing Raman spectroscopy in combination with machine learning analyses to improve diagnostic measures for identifying an assortment of diseases, including cancers, viral and bacterial infections, neurodegenerative and autoimmune disorders, and more. This review highlights the work accomplished since 2018 which focuses on using Raman spectroscopy and machine learning to address the need for better screening and medical diagnostics in all areas of disease. A critical evaluation considers both the benefits and obstacles of utilizing the method for universal diagnostics. It is clear based on the evidence provided herein Raman spectroscopy in combination with machine learning provides the first glimmer of hope for the development of an accurate, inexpensive, fast, and non-invasive method for universal medical diagnostics.