Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, surface quality, and overall process efficiency. While ML models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. This study bridges the gap by validating a ML-based chatter detection system using both simulated and real-world data. We apply a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1,600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. Our results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.