From Waste to Smart Transformations: AI-Driven Biomedical Waste Management
Radha K1, Vijayanarayanan N2, Col. Varun Bajpai3, Sridevi K.4
1Radha K, Principal, College of Nursing, SGPGIMS, Lucknow, (UP), India.
2Vijayanarayanan N, Principal, BSM College of Nursing, Lucknow, (UP), India.
3Col. Varun Bajpai, (VSM), Executive Registrar, SGPGIMS, Lucknow (UP), India.
4Sridevi K, Professor, Sri Vijaya Vidyalaya College of Nursing, Dharmapuri. TN.
Manuscript received on 17 October 2025 | First Revised Manuscript received on 18 January 2026 | Second Revised Manuscript received on 02 February 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 12-20 | Volume-13 Issue-3, March 2026 | Retrieval Number: 100.1/ijies.K1127120111125 | DOI: 10.35940/ijies.K1127.13030326
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Introduction: Biomedical waste (BMW) management is crucial for mitigating environmental and human health risks. Conventional methods, which include segregation, collection, transportation, and disposal, often fail to address the growing volumes of waste and the associated hazards. Aim: This article examines existing regional and worldwide practices, trends, challenges and the way forward in BMW management. Methodology: Peer-reviewed publications, conference papers, systematic reviews, and reports published in English that were searched using databases such as PubMed and Google Scholar were among the sources of information that were been synthesized in this review. Search terms included “waste management,” “medical waste management,” “smart bins,” “AI,” “machine learning,” and “IoT.” Results: Globally, disparities in BMW management practices persist, influenced by socio-economic conditions, regulatory frameworks, and resource availability. Developing regions often lack adequate infrastructure, leading to improper waste segregation, unsafe transportation, and open dumping, thereby exacerbating health and environmental risks. With approximately 75–90% of BMW being non-hazardous and the remainder requiring specialized handling, technological advancements. In India, for instance, it generates 1.5–2 kg of waste per bed daily, with an additional surge during the COVID 19 pandemic. Conclusion: Emerging AI-enabled solutions, such as smart bins, real-time monitoring, route optimisation, and blockchain technologies, demonstrate the potential to enhance efficiency, safety, and sustainability in BMW management. From Waste to Smart Transformations, AI-driven biomedical waste management has become a critical necessity at the global and regional levels, underscoring the urgent need for further extensive research in this field.
Keywords: Biomedical Waste Management, Artificial Intelligence, Machine Learning, IoT Things, Health Workers, Sustainability.
Scope of the Article: Biomedical Engineering