Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the way we understand and reconstruct the past. A growing area of research involves predicting the complete forms of broken or partial archaeological artifacts using advanced computational techniques. Modern AI tools can "see" and process artifact fragments with more precision and at scale. Algorithms like Mask R-CNN and YOLO are trained to detect and segment individual fragments from images or 3D scans, isolating them for further study. Machine learning enhances the process of matching and aligning fragments, with traditional ML algorithms like Support Vector Machines or Random Forests assessing whether fragments fit together based on their features. Deep learning for registration allows for better reconstructions even with significant data loss. AI for shape completion and generation is a powerful AI capability, with Statistical Shape Models (SSMs) learning common shape variations from complete artifacts and using that knowledge to estimate missing parts in incomplete samples. Deep Generative Models (GANs and VAEs) can generate likely completions for fragmented pieces, effectively "hallucinating" the lost geometry. AI doesn't work in isolation—it can integrate archaeological knowledge for smarter reconstructions, such as expert systems and Bayesian Networks. Real-world applications include pottery reconstruction, sculpture completion, and stone tool assembly. However, challenges remain, such as data availability, artifact diversity, subjectivity, and computational demand.

Similar to Dr. Qureshi's view, for effective image recognition, Convolutional Neural Networks (CNNs) can classify and identify patterns on artifacts, such as pottery styles, tools, and decorative items. While Deep Learning Models like VGG16 have shown high accuracy in classifying and identifying markings on bones and other artifacts. Other technique like Support Vector Machines can categorize artifacts into time periods based on physical attributes. Each technique has it's own challenges and you will have to decide on the one which works perfectly for you.