AI-Based Drug Abuse Detection: A Survey of Artificial Intelligence Approaches for Early Identification and Intervention

Drug abuse among adolescents and young adults has emerged as a serious public health and social concern, leading to adverse effects on physical health, academic performance, and emotional well-being. Early detection of substance abuse remains challenging due to social stigma, lack of awareness, and the absence of scalable screening mechanisms. Conventional clinical detection methods are invasive, expensive, and unsuitable for continuous monitoring. Recent advances in Artificial Intelligence (AI) have enabled automated, non-invasive, and privacy-aware approaches for early drug abuse detection. This survey presents a structured review of existing AI-based drug abuse detection techniques, including behavioral and linguistic analysis, decision-based severity modeling, and neural network-based content detection. The paper highlights the strengths and limitations of current approaches and emphasizes the need for integrated and ethically deployable AI-assisted screening frameworks.