Over the past decade, more than a thousand new psychoactive substances (NPSs) have emerged worldwide. This rapid proliferation of “designer drugs” poses significant challenges for drug control, forensic analysis, and public health. Artificial intelligence (AI) has increasingly been applied to address these challenges in NPS design and analysis. This review provides a comprehensive overview of AI methodologies—including deep learning, generative models, and quantitative structure–activity relationship (QSAR) modeling—and their applications in the synthesis, prediction, and identification of NPSs. We discuss how AI-driven generative models have been used to design novel psychoactive compounds and predict their pharmacological activity, how QSAR models can forecast potency and toxicological profiles, and how machine learning is enhancing analytical chemistry workflows for NPS identification. Special emphasis is placed on mass spectrometry (MS)-based techniques, where AI algorithms (e.g., for spectral prediction and pattern recognition) are revolutionizing the detection and characterization of unknown NPSs. A dedicated section examines the legal and regulatory implications of AI-generated psychoactive substances in the European Union (EU) and United States (USA), highlighting current policies, potential gaps, and the need for proactive regulatory responses. The review concludes with a discussion of the benefits and limitations of AI in this domain and outlines future directions for research at the intersection of AI, analytical chemistry, and drug policy.
