Thedemand of condition monitoring of induction motors (IM) is progressively increasing to maintain the performance of several important sectors in industry. This issue is of great importance since it prevents IM from failing and breaking down. As most of IM faults occur in bearings, the bearing fault detection (BFD) has become the main topic targeting the optimization of unscheduled downtime and maintenance cost of IM. Besides, emphasizing the causes and predicting failure consequences depend on the identification of the fault type. This paper is motivated by the advances in signal processing techniques and machine-learning systems. This study proposes a novel hybrid approach for BFD based on Optimized StationaryWavelet Packet Transform for feature extraction and artificial immune system nested within support vectors machines for fault classification. The motor current signatures analysis offers a cost-effective method for BFD. To evaluate the approach, the current signals were collected under various bearing conditions and load levels. The experiment results prove the efficiency of the proposed approach.