This research uses natural language processing (NLP) methodologies to quantitatively analyze key religious and philosophical texts by identifying language trends, themes, and sentiment. Using a combination of text-mining techniques, topic modeling, and sentiment/emotion analysis, we evaluate how ideas, values, and emotions are conveyed within religious and philosophical traditions, including the Bible, Quran, Bhagavad Gita, and classic philosophy texts. The research analyzes publicly available text corpora and translations to quantify word counts, identify topic trends, and analyze emotional trajectories across chapters and verses. The comparative analysis reveals differences in thematic focus, emotional tone, and rhetorical style across religious and philosophical texts, and across translations of the same texts. The study's aim is to show the efficacy of computational methods as a complement to traditional textual scholarship by developing new ways to analyze form, sentiment, and meaning of primary texts. The interdisciplinary study and research also aim to contribute to emerging dialogue between the fields of digital humanities, linguistics, and religious studies to provide frameworks for large-scale, digital, and data-based analysis of sacred texts and literature.