The safe and efficient management of Biomedical Waste (BMW) is critical to safeguarding public health and protecting the environment. With the increasing volume of waste generated by healthcare facilities, traditional manual waste handling methods are proving inadequate, often leading to improper segregation, delayed disposal, and elevated risks of contamination. The objective of paper is to present an AI-enabled framework for sustainable biomedical waste management that integrates Internet of Things (IoT) sensors, real-time data analytics, and predictive algorithms to monitor and manage waste streams. The system uses AI models to assess sensor inputs such as gas emissions, bin weight, and fill levels to identify potential hazards, predict overflow events, and generate timely alerts. Cloud-based dashboards offer centralized monitoring and compliance tracking, facilitating improved operational efficiency and regulatory adherence. Results from a prototype implementation indicate enhanced responsiveness, reduced human error, and improved environmental compliance, thereby highlighting the potential of AI-driven systems in transforming biomedical waste handling into a more sustainable and intelligent process.