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Introduction: Ayurvedic toxicology (Agada Tantra) provides an extensive understanding of Visha dravyas and their therapeutic transformation in polyherbal and herbo-mineral medicines. Conventional pharmacovigilance systems often fail to accurately model the multidimensional interactions among diverse phytochemicals, metals, minerals, and bioactive toxins. Quantum Artificial Intelligence (Quantum-AI), particularly Quantum Machine Learning (QML), offers advanced computational capabilities to process high-dimensional, non-linear, and probabilistic toxicological data. Integrating quantum computing with Ayurvedic toxicology may redefine predictive safety assessment and rational drug design in complex traditional formulations. Objectives: To explore the conceptual and practical synergy between Quantum-AI and Agada Tantra; evaluate QML in modelling intricate toxicological interactions of formulations containing Visha dravyas; propose a computational framework for safety prediction, dose optimization, and interaction mapping; and examine ethical and epistemological implications of quantum-enabled analytics in Ayurveda. Methods: A narrative–systematic hybrid review integrating classical Ayurvedic treatises (Bṛhattrayī, Laghutrayī, Rasaśāstra) with contemporary literature in computational toxicology, quantum information science, and machine learning. Conceptual modelling, bibliometric mapping, and theoretical simulations using quantum kernels, variational circuits, and quantum neural networks were synthesized. Traditional detoxification processes (Śodhana, Māraṇa) were compared with computational data-purification analogues. Results: Theoretical synthesis suggests that QML algorithms, including Quantum Support Vector Machines and Variational Quantum Classifiers, may model multi-component toxicity networks more efficiently than classical AI. Quantum principles of superposition and entanglement conceptually parallel Ayurvedic notions of Yogavāhitva and Guṇa-Sāmya, supporting predictive adverse-effect detection and detoxification optimization. Conclusion:
Quantum-AI integration holds significant promise for decoding complex toxicological interactions in Ayurvedic formulations, fostering safer drug validation, innovative research pathways, and a modern computational lexicon for Agada Tantra.

Keywords

Quantum Machine Learning, Agada Tantra, Visha Dravya, Polyherbal Toxicology, Herbo-Mineral Formulations