Machine Learning In The Study Of Ancient Civilizations: Transforming Archaeology, Linguistics And Historical Analysis

The study of ancient civilizations involves analyzing complex, fragmented and heterogeneous datasets, including inscriptions, archaeological remains and genetic evidence. Traditional methods of analysis are time-consuming and limited in scalability. In recent years, Machine Learning (ML) has emerged as a transformative tool that enables automated pattern detection, classification and reconstruction, significantly accelerating discoveries in archaeology and historical studies. This research article examines the application of ML techniques in the analysis of ancient civilizations across domains such as archaeology, linguistics and genomics. Using a narrative review approach based on PRISMA-ScR guidelines, the study synthesizes findings from 28 empirical studies conducted between 2018 and 2026. Evidence indicates that ML techniques such as deep learning, convolutional neural networks (CNNs) and self-organizing maps (SOMs) have achieved accuracy levels ranging from 80% to 95% in tasks such as site detection, script recognition and artifact restoration. Key applications include satellite-based site detection, decipherment of undeciphered scripts such as the Indus Valley script and reconstruction of damaged artifacts using generative models. While these advancements offer significant potential, challenges such as limited datasets, ethical concerns and interpretability issues remain. The study concludes that ML represents a paradigm shift in the study of ancient civilizations, enabling faster, more accurate and interdisciplinary research outcomes.