Artificial intelligence (AI) has significantly advanced ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction, a critical aspect of drug design. Traditional experimental evaluations are costly and time-intensive, often hampering drug development timelines. AI-based models, leveraging machine learning (ML) and deep learning (DL) techniques, provide a more efficient and accurate alternative by correlating chemical structures with ADMET properties. These models support high-throughput screening, mitigate clinical trial failures, and enhance drug discovery success rates. ML methods, such as random forests and support vector machines, rely on molecular descriptors and fingerprints for prediction. However, they often lose structural information due to feature simplification. Deep learning approaches, including graph neural networks (GNNs), address these limitations by automatically learning complex molecular features from raw data, significantly improving predictive accuracy. Platforms like ADMETlab and SwissADME exemplify the practical integration of these methodologies, providing tools for bioavailability, toxicity, and pharmacokinetics analysis. As research progresses, advancements in multitask learning, explainability, and uncertainty estimation are crucial for refining these models and enhancing their reliability. AI-powered ADMET prediction is reshaping pharmaceutical innovation by reducing costs, improving efficiency, and driving success in drug development pipelines.