Transforming Medical Waste Management Through IoT and Machine Learning: A Path Towards Sustainability

The systematic process of detecting, gathering, and disposing of all waste in a specific manner so as not to jeopardize human or environmental life is known as waste management. If not correctly managed, medical waste, which includes items like old needles, sharps, and infectious materials, poses a serious risk to the environment and the general public's health, especially in developing and underdeveloped countries. In this study, we used IoT and machine learning technologies to automate waste identification, information tracking, and monitoring, improve the accuracy and efficiency of waste bins, and enable real-time monitoring and analysis of waste information. By utilizing smart waste bins fitted with sensors and machine learning algorithms to automatically detect and classify various forms of waste, IoT and machine learning are being applied to the management of medical waste. These intelligent bins can then notify waste management staff when they need to be emptied and can offer useful information on waste streams for analysis and practice improvement. Only persons who have been authenticated will be permitted to collect medical waste, and the system will keep a record of all data. Using IoT and machine learning technologies, we attempted to reduce the danger of unintentional exposure to hazardous materials, which enhanced overall public health and safety in addition to increasing the effectiveness of medical waste management. Overall, compared to current manual medical waste management systems, the convergence of IoT and machine learning has the potential to significantly increase the sustainability and efficiency of medical waste management, reducing the environmental impact of healthcare operations and enhancing public health.