The suspension system plays a critical role in vehicles, providing both comfort and directional control. Therefore, it is essential to implement a monitoring system to ensure the proper functioning of suspension components, as a failure in any of these components can lead to accidents. Furthermore, monitoring the condition of the suspension system helps in maintaining its performance and minimizes maintenance costs. Traditionally, diagnosing faults in suspension systems has relied on specialized setups and vibration analysis. Alternatively, deep learning-based approaches for fault diagnosis in suspension systems offer a promising solution by enabling faster and more accurate real-time fault detection. This study investigated the use of vision transformers as an innovative approach to fault diagnosis in suspension systems, leveraging spectrogram images. Spectrogram images from vibration signals were extracted and used as inputs for the vision transformer model. Test results showcased a remarkable 99.39% accuracy in fault identification, affirming the system's effectiveness.