This research is essential for filling the critical gap in contextualized historical text analysis: it requires that we extract insights from large scale, landed structured historical documents without losing or reducing the contexts in which phrases were used. Traditional methods often fall short in exposing the intricate socio-cultural dynamics nested in these texts. This study presents an end-to-end machine learning framework that combines NLP, sentiment analysis and deep learning techniques to automatically identify patterns, sentiments, and semantic nuances in ancient texts. The approach includes prepossessing significant historical data, feature engineering, and model building. Experimental data validate that, relative to traditional methods building model outperforms by some margin which is significant as shown in the key findings. To illustrate, the model could identify emotional undertones in the sentiment analysis had an accuracy of 92%, while spice fell short at just 76%. Further, this ML engine achieved an 88% precision in latent themes contractions compared to conventional thematic analysis methods with an average of 67%. Moreover, machine learning could transform the experience of historical research by allowing historians to discover hitherto unseen patterns and narratives. It also increases the speed and effectiveness of historical analysis, adding a component to studying socio-political events. The findings of the study demonstrate considerable potential for AI-based instruments in the humanities, setting the stage for further interdisciplinary work uniting computational techniques and historical research.