This chapter explores the transformative impact of artificial intelligence (AI) on the diagnosis, classification, and treatment of orbital and eyelid diseases. By integrating machine learning (ML) and deep learning (DL) algorithms with imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and external ocular photography, AI systems are enabling highly accurate and efficient assessments of orbital pathology. Applications include automated measurement of eyelid morphological parameters, improved surgical planning for ptosis and reconstructive procedures, and enhanced management of thyroid-associated ophthalmopathy (TAO) and lacrimal disorders. The chapter also highlights how convolutional neural networks (CNNs), U-Net architectures, and generative adversarial networks (GANs) contribute to image segmentation, disease prediction, and postoperative outcome modeling. Through these innovations, AI is advancing precision medicine in oculoplastic and orbital surgery, promoting individualized care and improving visual and aesthetic outcomes for patients.