From Waste to Smart Transformations AI-Driven Biomedical Waste Management

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 percent 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.