Abstract: The rapid development of artificial intelligence (AI) is profoundly reshaping the traditional paradigm of chemical safety assessment. This paper systematically reviews the research progress and application status of AI, particularly machine learning and deep learning methods, in the field of computational toxicology. From three core dimensions—data foundation, algorithm system, and model interpretability—the evolutionary path of the AI-driven safety assessment technical paradigm is summarized. Combined with typical application scenarios such as drug research and development, cosmetic safety evaluation, and the control of emerging environmental pollutants, the role transformation of related technologies from "alternative experimental methods" to "safety-enabled design tools" is discussed. On this basis, the paper further analyzes the acceptance and standardization progress of computational toxicology methods in international regulatory frameworks, and explores the key challenges faced by AI models in terms of interpretability, complex system assessment, system integration, ethics, and standardization. In summary, this paper sorts out the context of technological evolution and proposes a conceptual framework for intelligent toxicology systems, aiming to provide a systematic reference and forward-looking perspective for the scientific research and regulatory application of AI-driven chemical safety assessment technologies.