Machine learning (ML) has emerged as a powerful tool in biomedical diagnostics. It enables the identification of subtle spectral patterns that are otherwise undetectable through conventional analytical methods such as fluorescence spectroscopy. In biosensing, Surface-Enhanced Raman Scattering (SERS) has demonstrated immense potential for detecting biomolecules at ultra-low concentrations with high specificity and sensitivity. The development of SERS-based nanosensors that amplify Raman signals upon biomolecule interaction has been extensively explored for disease diagnostics and prognostics. However, the complexity of SERS spectra necessitates advanced computational approaches for meaningful and accurate interpretation. This study leverages a multitude of ML algorithms to analyze and classify SERS spectra, enhancing the accuracy of SERS nanotags for disease detection. As a specific example, we use SERS spectra from biomineralized gold nanoparticles synthesized with various amino acids. We hypothesize that while these spectra cannot be interpreted visually, machine-learning approaches can be used to decode them. The spectral variations arising from the biomineralization process encode valuable biochemical information, which we systematically decipher using unsupervised and supervised ML models. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) aid in dimensionality reduction, while classification algorithms, including Support Vector Machines (SVM) and Random Forests, improve biomarker differentiation. Subsequently, we aim to extend this approach to distinguish between (i) multiple endogenous proteins of disease significance (e.g., albumin, transferrin, fibrinogen), and (ii) tumor cells from various origins (lung, liver, breast, ovarian), comparing them against healthy cell counterparts. This research develops an AI-driven computational framework integrating ML with SERS for real-time, noninvasive disease diagnostics in clinical settings.