Rolling element bearings are critical components in rotating machinery, essential for a wide range of applications, from small household devices to large industrial machines. Localized defects, such as pits and spalls, can develop on bearing surfaces due to cyclic loading, leading to potential machinery breakdowns and production losses. Early detection of these defects is crucial to prevent such failures. This study proposes a novel technique for bearing fault classification using Wavelet Scattering Transform (WST). WST, leveraging principles from signal processing and wavelet transforms, extracts features that are stable under minor deformations and time-invariant. The proposed methodology was validated using experimental data from the Case Western Reserve University Bearing Data Centre. Results indicate that WST-based feature extraction, when combined with various classification techniques, enhances the accuracy of bearing fault diagnosis, providing a robust tool for early defect detection.