AI-Driven Sustainable Medical Waste Management A Conceptual Optimization Framework

The rapid expansion of healthcare services has dramatically increased the generation of medical waste, posing serious problems for the environment, the economy and public health. Conventional medical waste management systems are still mostly reactive, based on static scheduling, manual separation and cost-based routing strategies that do not consider sustainability. To overcome these shortcomings, an AI-based conceptual optimization approach covering predictive analytics, intelligent segregation and sustainability-oriented routing is proposed in this study in a unified decision support architecture. A PRISMAbased systematic screening of 85 research articles was conducted from which 21 high relevance studies were selected for identifying critical research gaps. The results of the analysis found that the existing works mainly focus on waste characterization or AI-based classification, routing optimization, or sustainability assessment separately without combining the key elements into one. The proposed model incorporates waste generation forecasting into a multi-objective vehicle routing formulation that optimizes operational cost, vehicle travel distance and carbon emission simultaneously. By connecting the outputs of AI-driven predictions and optimization decisions, the framework makes it possible to improve proactive scheduling, vehicle utilisation, compliance with regulations and reduce environmental impact. The framework offers a flexible and scalable decision support architecture for sustainable medical waste management in smart healthcare systems and serves as a solid basis for its future computational implementation and validation in real-world applications.