The work outlined here provides a new method to handle fault of Power Electronic Traction Transformer (PETT) switch using reverse charging Cascaded H Bridge (CHB) and Dual Active Bridge (DAB) topologies. Precisely, the main purpose is to improve the speed of detection, determining the location, and recovery of faults from the existing system using feature extraction and Machine Learning algorithms. The traditional approaches in achieving fault tolerance are defective in detecting faults in good time, isolating faults inadequately, and using backup hardware. In order to solve these problems, the proposed methodology actively reassigns control signals to backup modules resulting in the exclusion of faulty elements while preserving a stable system performance with moderate loss in efficiency. The feasibility of the suggested approach is confirmed through simulation outcomes for fault detection precision, which is increased to 98%; the fault localization time of at most 5–10 ms; and system throughput of 5–8%. Furthermore, the work investigates how CHB and DAB function in fault conditions and enshrine a novel reverse charging method for maintaining the DC voltage of the redundant module. The startup process of the PETT system is also managed with optimization of voltage and transient, which leads to enhance the general system initialization. Besides increasing the dependability and fault tolerance of PETT systems, the above methodology also reduces the system’s embedded hardware duplication and elevates system performance and scalability, which consequently leads to the decrease of the total system cost by 15%. These results point out that the proposed solution has potential for the development of the next generation of fault-tolerant power electronic systems.