The present study aims to investigate, examine, and analyse the great potentials of AI techniques to analyse and understand archaeological and historical data uncovering different aspects related to the Roman society in ancient times. To achieve this goal and purpose, a mixed method was used integrating a variety of data and relying on the qualitative and quantitative analysis through artificial intelligence algorithms techniques, including natural language processing, machine learning, and computer vision techniques. The analysed data and information include inscriptions and written documents, pottery remains and coins, and archaeological images and layouts. The result of this study illustrates and shows the efficacy and high quality of artificial intelligent techniques in providing new insights and deeper understanding of different aspects of Roman society, such as gendered roles, the social interactions of and between classes, patterns of economic exchange and trade, the political and religious importance of several major monuments and buildings in Rome. The results also reflect the high-reliability and accuracy of these techniques in analysing the data, such as the classification accuracy using machine learning algorithms 92.8%, and the dating accuracy average is 88.2%. The study concludes with recommendations of future studies and developments g focused on addressing and treating the challenges and limitations and enhancing the potentials of AI techniques in analysing archaeological and historical data. The present study opens new attention and commitment towards using advanced AI techniques in analysing and exploring ancient Roman society and culture and civilizations.