The increasing ethical concerns, high costs, and limited translational predictability of animal experiments have accelerated the development of alternative approaches in preclinical pharmacokinetics (PK) and toxicology. Artificial Intelligence (AI) has emerged as a transformative technology that integrates computational modeling, in vitro systems, organ-on-chip platforms, and large biological datasets to predict drug behavior and toxicity without extensive animal use. AI-driven methods such as Quantitative Structure–Activity Relationship (QSAR) modeling, Physiologically Based Pharmacokinetic (PBPK) modeling, machine learning algorithms, digital twins, and multi-omics analysis enable rapid and accurate prediction of absorption, distribution, metabolism, excretion (ADME), and toxicological endpoints. These innovative approaches support the principles of the 3Rs (Replacement, Reduction, and Refinement) while enhancing drug development efficiency. This review discusses current AI-integrated alternatives to animal testing, their applications in pharmacokinetics and toxicology, advantages, limitations, and future perspectives in regulatory science.