This study examines the epistemological and ontological challenges emerging from the intersection of data science and law. Through a multidisciplinary lens, it analyzes how predictive and language models in the legal domain challenge traditional legal theory and redefine key legal categories. The research highlights the tension between data-driven approaches and conventional legal reasoning, emphasizing the need for a reflexive legal rationality. This framework aims to critically integrate data science insights while maintaining the nuanced interpretative nature of legal thought. The article explores the implications of algorithmic decision-making in law, addressing issues of transparency, accountability, and the changing nature of legal knowledge. The methodology employs a multidisciplinary approach, combining conceptual analysis and literature review. By proposing a balanced approach that harnesses the power of data science without compromising legal principles, this study contributes to the ongoing dialogue on responsible integration of technology in legal practice and theory.