Industry 4.0, in its search for improvements in processes and efficient products, has increasingly invested in the use and development of high-performance materials for its production lines. We have seen this, with the introduction of CFRP in the aeronautical industry, since these composite materials have reduced the weight of aircraft and improved their performance. For the construction of large structures, drilling processes are also necessary to fix the parts. However, this machining process can end up causing failures in the structure as a whole. These structural failures occur due to fragmentation, tearing, or detachment of the matrix fiber, significantly reducing the quality and reliability of the final equipment. In this scenario, it is important to preventively detect these intrinsic production failures that end up condemning the final parts. One of the indirect detection methods is through acoustic emission. This work presents a feasibility study focused on the application of data-driven methods for delamination detection and tool wear monitoring in composite machining. A setup for a helical interpolation end milling drilling process were performed under varying machining conditions, from mild to severe, on CFRP composite plates. Acoustic emission (AE) signals were acquired at each machining pass. The methodology involved selecting an optimal frequency band, to obtain information about the wear of the drilling tool, through RMS and power spectral density (PSD) analysis, followed by using correlation indices to characterize tool wear progression. The results demonstrate the potential of spectral and statistical techniques to support real-time monitoring and decision-making in advanced composite manufacturing.