Artificial intelligence (AI) has become the central catalyst of twenty-first-century scientific transformation, redefining both the methodologies and the philosophical assumptions that have guided scientific inquiry for centuries. Historically, scientific discovery emerged through human-driven cycles of observation, hypothesis formation, experimentation, and interpretation. These processes were limited by the cognitive capacity, time constraints, and perceptual boundaries inherent to human researchers. However, the rapid evolution of AI—particularly machine learning, deep neural networks, and generative intelligence—has radically shifted this landscape. AI systems now possess the ability to analyze unprecedented volumes of data, identify relationships invisible to human cognition, construct predictive models of complex phenomena, and autonomously refine their internal logic through iterative learning. As a result, the boundary between human reasoning and computational inference has begun to blur, inaugurating a new era in which machines participate actively in the epistemic process of scientific discovery.
The purpose of this study is to explore the multi-dimensional influence of AI-driven innovation on the future of scientific discovery. This includes an analysis of the conceptual foundations of AI-based reasoning, the structural transformation of research methodologies, the cognitive implications of human–machine collaboration, and the ethical challenges introduced by algorithmic participation in scientific knowledge creation. In fields such as molecular biology, astrophysics, materials engineering, climate science, and medical diagnostics, AI systems now routinely perform tasks once considered exclusive to human intellect—interpreting complex datasets, developing data-driven hypotheses, and optimizing experimental design. Breakthrough systems such as DeepMind’s AlphaFold, OpenAI’s generative models, and IBM’s Watson demonstrate not only AI’s capacity to accelerate the pace of discovery but also its ability to reshape the logic of inquiry, shifting science from a linear, hypothesis-led process into a dynamic, data-centric and feedback-driven ecosystem.