Introduction: Efficient detection of subsurface damage (SSD) is essential to ensure the service life and performance of machined components, yet conventional identification methods are predominantly destructive. Elastic Emission Machining (EEM) stands out as a non-contact process that removes material at the atomic scale through chemical reactions, producing mirror-like finishes. This technique can be utilized for SSD detection by generating spherical cap-shaped imprints that allow access to and evaluation of the material's integrity below the surface. Methods: This study presents an experimental analysis of the EEM process on glass specimens using piezoelectric diaphragm (PZT) sensors for in situ signal acquisition. A 2^k-p fractional factorial design was implemented to evaluate the influence of variables such as tool rotation (47 to 67 Hz), tool material hardness, tool finish (CNC vs. ultra-precision), applied load (260 and 520 g), and testing time. Signals were processed using Fast Fourier Transform (FFT) and Root Mean Square (RMS) analysis to extract quantitative process indices. Results: The results demonstrated that the tool's surface finish is directly transferred to the workpiece, altering the texture of the generated caps. Frequency analysis revealed that most caps presented predominant peaks in the 20 to 32 Hz range. Notably, for tool rotations above 60 Hz, the highest peak frequency shifted to the 31–33 Hz range, or approximately 2512 Hz. Furthermore, trials that exhibited peaks at higher frequencies were correlated with lower average RMS values. Conclusions: The use of PZT sensors proved to be an effective and low-cost method for monitoring the EEM process. The generated indices successfully correlate acoustic signal characteristics with machining parameters, providing a solid perspective for future subsurface integrity characterization.