Machine learning (ML) has reshaped scientific practice across disciplines, yet its epistemic consequences remain poorly understood. This paper analyzes how its broad diffusion reconfigures the conditions under which scientific claims are produced and evaluated. Using a hierarchical taxonomy of 255 ML techniques and embedding-based semantic mapping, we analyze 4.9 million scientific publications from OpenAlex (1990-2025). We reconstruct the semantic space of ML research and show a core-periphery structure, with physical sciences forming the methodological core and health sciences representing the primary growth area. We identify distinct methodological profiles across domains: predictive techniques concentrate in computer sciences while inferential approaches remain distributed across applied fields, reflecting historically differentiated validation regimes. We observe the displacement of inference-oriented techniques by predictive architectures in domains that have traditionally prioritized interpretability-most notably health sciences and social sciences. This displacement unfolds in two qualitatively distinct waves. The first (2015-2021) was driven by deep learning architectures that reduced predictive error while introducing epistemic opacity. The second (post 2022) is organized around a small number of architectures delivered through external companies, introducing a further layer of opacity over data and processes that researchers cannot access or report. This transformation expands the analytical capacity of science, and also reorganizes the conditions under which scientific knowledge can be produced and evaluated.

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