The growing power of computational algorithms, including AI, has brought the scientific process into a new phase, making democratization broadly usable by both humans and machines. This study addresses the convergence of AI technology and literary analysis, examining how AI may offer new approaches to analyzing literature. Traditional literary criticism heavily depends on human subjectivity, which is constrained by individual biases and human cognition limitations. AI, with its ability to analyze large data sets and detect patterns beyond human perception, offers an opportunity to supplement and enhance traditional methods. This study explores AI techniques, including natural language processing and machine learning algorithms, for analyzing literary texts. The goal is to identify patterns, themes, and structures that human critics may miss. It also examines the implications of AI-generated interpretations on the subjective nature of literature, questioning whether machine interpretations can be considered valid and how they influence our understanding of texts. Incorporating AI into literary studies presents challenges, including the need for algorithm transparency and the potential lack of human sentiment in literary evaluation. This study advocates for a collaborative approach, using AI as a supplementary tool for literary scholars to navigate challenges and enhance their analysis. The objective of this paper is to demonstrate how technological advancements can offer new perspectives, enriching literary studies and broadening the scope of interpretation in the digital age.