Advanced Structural Health Monitoring (SHM) systems are essential for aging aerospace infrastructure and Carbon Fiber Reinforced Polymer (CFRP) structures. Though Lamb wave-based Non-Destructive Testing (NDT) effectively monitors CFRP, traditional methods struggle with complex wave patterns, environmental variations, and large data volumes from continuous monitoring. This research overcomes these limitations by developing an AI system that integrates Lamb wave testing with Vision Transformer. The approach captures Lamb wave signals via actuators and sensors settled on CFRP structures, converting them into Continuous Wavelet Transform (CWT) inputs, and automates damage identification. This framework improves detection accuracy and reliability, enabling real-time assessment.