Artificial intelligence (AI) carries a transformative potential in literary analysis, and the current study aims to explore AI application in literary analysis, focusing on thematic investigation and narrative structure in Virginia Woolf’s Mrs. Dalloway and Ralph Ellison’s Invisible Man. However, through the use of the most recent Natural Language Processing (NLP) models (BERT, GPT), and sentiment models (VADER), this work analyzes thematic patterns, emotional routes, and symbolic complications. The study integrates transformer-based models alongside traditional methods such as TFIDF and Doc2Vec for a multi layered examination of the texts. The findings of the study show the ability of AI to find thematic motifs, trace sentiment arcs, and spot recurring symbols and help understanding of the character development and societal theme development. Despite that, the study reveals that AI is still not completely able to interpret abstract literary features, like metaphors and allegories, which calls for cultural sensitivity, as well as interpretive context. This research presents how AI can extend traditional close reading for both scalability and analytical depth and contributes to digital humanities. This further augments the centrality of human AI partnership for addressing interpretive challenges and ensures ethical, inclusive approaches to computational literary studies.