Animal experimentation has historically supported biomedical and toxicological research; however, its limitations in accurately predicting human responses, combined with ethical and regulatory pressures, have driven the development of alternative methods. Advances in artificial intelligence (AI) offer a promising opportunity to not only reduce animal use but also significantly enhance the reliability, efficiency, and human relevance of toxicity and safety assessments. This review examines AI-driven approaches - including in silico modelling, machine learning, and computational toxicology - used to predict toxicity, assess drug safety, and classify chemical hazards, with a focus on their contribution to the 3Rs principle and regulatory innovation. AI models, including deep learning algorithms, quantitative structure-activity relationship models, and integrated decision strategies, have demonstrated improved accuracy in predicting endpoints such as skin sensitization, carcinogenicity, and endocrine disruption. Moreover, hybrid methods combining in vitro data with AI-powered tools provide a scalable and reproducible framework for safety evaluation. While regulatory validation remains a challenge, the convergence of AI and toxicology holds immense potential to advance both predictive science and animal-free research. AI represents not only an ethical alternative but also a scientifically superior path toward safer and more human-relevant toxicity testing.