In the era of Industry 4.0, predictive maintenance has emerged as a key enabler for improving reliability, reducing downtime, and optimizing asset performance in smart manufacturing environments. Rolling element bearings, critical components in rotating machinery, are susceptible to degradation that can lead to costly failures if not detected early. This study presents an unsupervised machine learning framework for automated anomaly detection in bearing condition monitoring data in an Industry 4.0 environment. Vibration signals were acquired from the motor drive end of an industrial circulating water pump and processed using envelope demodulation to enhance sensitivity to bearing-related impacts. A comprehensive set of time-domain and spectral features, including RMS, kurtosis, crest factor, spectral entropy, and dominant frequency, was extracted from 60 quarterly measurement snapshots. Isolation Forest and One-Class SVM models were applied to the engineered feature set to compute anomaly scores, with results compared against global RMS (GRMS) trends. The analysis revealed consistent alignment between detected anomalies and elevated GRMS levels, indicating the effectiveness of the proposed approach in identifying potential fault progression. The framework demonstrates a scalable, sensor-driven anomaly detection strategy suitable for real-time deployment in Industry 4.0 predictive maintenance systems.