In the expansion of Industry 4.0, many automation processes are being enhanced as means of accomplishing higher productivity goals. With the prospect of new achievable objectives, the demand for faster and more reliable resources processing methods are also needed. Similarly, machining processes have also been improved with the development of IoT devices by streamlining operations, enabling predictive maintenance, and providing real-time data for better decision-making, collaborating with such productivity levels. For instance, in metal milling, IoT-based sensors techniques are being developed and proved efficient in increasing speed and reliability, whereas reducing system invasiveness and complexity, which grants more profitability. The present paper proposes a real-time metal roughness average (Ra) analysis method based on Acoustic Emission (AE), which indi-rectly estimates roughness through signal processing and feature extraction of the EA signal through Power Spectral Density (PSD) evaluation. The experimental setting con-sists of a steel workpiece in which straight lines were milled with four distinct roughness levels (6 μm, 12 μm, 18 μm and 24 μm, produced by defined milling parameters), and the method was able to estimate the Ra with error under 7%. This work aims to contribute to the real-time monitoring of surface roughness in alignment with Industry 4.0 require-ments, by demonstrating the effectiveness of IoT-based solutions, and the potential of Acoustic Emission in machinery sensing and process automation.