This chapter explores the transformative role of artificial intelligence (AI) in preclinical and translational research, focusing on its applications in drug development, predictive toxicology, pharmacokinetics, biomarker identification, and multi-omics integration. AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) are revolutionizing biomedical research by enhancing decision-making, reducing costs, and accelerating timelines. Key advancements include AI-driven toxicity prediction models like DeepTox, pharmacokinetics frameworks such as Chemi-Net, and multi-omics integration tools like MOFA and DeepMOCCA. These innovations enable precise biomarker discovery, patient stratification, and personalized medicine. Despite these achievements, challenges such as data quality, model interpretability, regulatory compliance, and ethical considerations persist. The chapter emphasizes the need for collaborative efforts to ensure AI’s ethical, explainable, and inclusive application in biomedical research, paving the way for improved translational success and public health outcomes. ​.