Autonomous robotics has revolutionary possibilities for visitor engagement and education when integrated into cultural heritage organizations. However, the implementation of smart museum guides is largely dependent on reliable computer vision systems that can recognize artifacts accurately and in real time. A deep learning framework for the multi-class categorization of Egyptian artifacts that is suited for intelligent museum robotics is presented in this paper. Three state-of-the-art architectures, ResNet18, EfficientNet-B0, and Vision Transformer (ViT-Base), were empirically evaluated in comparison using a carefully selected dataset of 4,782 photos from the Egypt Monuments Dataset. The platform enables automated, context-aware visitor assistance by enabling autonomous robots to identify important Egyptian artifact types, including statues, temples, pyramids, and royal busts. EfficientNet-B0 outperformed ViT-Base (96.3%) and ResNet18 (91.1%), according to experimental results, with a validation accuracy of 96.9%. Additionally, EfficientNet-B0 is the most practical model for real-time deployment in robotic systems with limited resources because it provides an ideal balance between accuracy, inference speed, and computational efficiency. This work advances intelligent human-robot interaction in museum settings and automated cultural heritage preservation.