Herbal medicines represent a valuable source of therapeutically active compounds and are increasingly incorporated into modern healthcare and drug discovery. However, their chemically complex composition, multi-target pharmacological effects, batch-to-batch variability, metabolic transformation, potential toxicity, and herb–drug interactions present significant challenges for systematic safety assessment. Conventional toxicological approaches are often time-consuming, costly, and dependent on extensive experimental studies, highlighting the need for predictive and mechanism-oriented strategies. Artificial Intelligence (AI), including Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP), has emerged as a promising approach for advancing herbal toxicology and safety evaluation. This review critically examines the application of AI-assisted approaches for toxicity prediction, absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment, herb–drug interaction prediction, mechanistic toxicology, and integrated safety profiling of herbal medicines. Particular attention is given to quantitative structure–activity relationship (QSAR) modelling, molecular docking, virtual screening, pharmacokinetic prediction, multi-omics integration, knowledge graphs, adverse-effect prediction, and explainable AI. These approaches can facilitate the early identification of potentially hazardous phytochemicals, predict organ-specific toxicity, identify toxicophoric structural features, evaluate metabolic liabilities, and characterize interactions with drug-metabolizing enzymes and transporters. Furthermore, integration of chemical, biological, pharmacological, clinical, and toxicological datasets may improve the prioritization of safer herbal candidates and support mechanism-informed risk assessment. Despite these advances, limitations related to data quality, dataset heterogeneity, model interpretability, external validation, uncertainty, and regulatory acceptance remain important challenges. Future integration of explainable AI, standardized toxicological datasets, multi-omics technologies, digital models, and experimental validation could establish more reliable and predictive frameworks for herbal medicine safety evaluation.