This study develops an IoT-based Tool Condition Monitoring (TCM) system for milling operations, aiming to enhance product quality through real-time tool wear detection. Utilizing an ESP-32 microcontroller and a vibration sensor positioned near the cutting tool, the system captures and analyzes vibration signals during the milling process. Advanced signal processing techniques extract relevant features to assess tool condition. Experimental trials demonstrate the system’s effectiveness, with a strong correlation (Pearson’s r = 0.77) between vibration-based predictions and actual tool wear measurements. This IoT-enabled TCM system offers a cost-effective solution for industrial milling applications, potentially improving product quality, reducing downtime, and optimizing tool replacement schedules. The research contributes to the advancement of smart manufacturing technologies, providing a practical approach to integrating IoT communications in machining processes for enhanced operational efficiency and product quality control.