The proposed model presents a comprehensive analysis of stator short circuit faults in Permanent Magnet Synchronous Motors (PMSMs). An electric motor known as a PMSM generates a magnetic field through the use of permanent magnets in its rotor. The motor rotates as a result of the interaction between this magnetic field and the rotating magnetic field created by the stator windings. This interaction produces torque. With the increasing integration of PMSMs in various industrial applications, the need for reliable fault detection and diagnosis techniques has become paramount. Stator short circuit faults are one of the most common and critical faults affecting the performance and reliability of PMSMs. In this research, we investigate the root causes, characteristics, and consequences of stator short circuit faults in PMSMs. We propose a novel fault detection and diagnosis approach based on advanced machine learning algorithms that is LSTM. The results indicate that the proposed approach achieves high accuracy and reliability in detecting and diagnosing stator short circuit faults in PMSMs, thereby enhancing the overall reliability and safety of PMSM-based systems. This research contributes to advancing the understanding of stator short circuit faults in PMSMs and provides practical insights for developing effective fault diagnosis strategies to mitigate their adverse effects.