Artificial intelligence is reshaping chemical research by linking high-dimensional analytical signals with molecular, biological, environmental, and pharmaceutical information. This review synthesizes literature from 2018–2026 on chemometrics, machine learning, deep learning, graph neural networks, transformers, foundation models, multimodal learning, and generative systems. It examines data obtained from spectroscopy, chromatography, mass spectrometry, electrochemical sensors, hyperspectral imaging, process monitoring, molecular descriptors, fingerprints, SMILES, graphs, three-dimensional structures, proteins, and omics. Environmental applications include contaminant detection and quantification, suspect and non-target screening, source attribution, fate and transport prediction, ecotoxicity assessment, wastewater-treatment evaluation, and ecological-risk prioritization. Pharmaceutical applications encompass raw-material authentication, quality control, impurity profiling, formulation and drug-delivery optimization, continuous manufacturing, real-time release testing, virtual screening, molecular design, and ADMET prediction. Across both domains, AI improves nonlinear pattern recognition, structure–signal translation, candidate ranking, and multi-objective optimization; however, sophisticated models do not consistently outperform well-designed chemometric approaches, particularly with small or biased datasets. Major barriers include class imbalance, limited chemical diversity, data leakage, instrument variability, missing metadata, weak external validation, poor uncertainty calibration, limited interpretability, and regulatory concerns. Future progress requires FAIR multimodal datasets, independent validation, applicability-domain analysis, explainable and uncertainty-aware models, digital twins, federated learning, autonomous laboratories, human oversight, and sustainable computing for trustworthy scientific deployment.