Healthcare facilities generate heterogeneous waste streams that must be accurately segregated at the point of disposal to mitigate occupational exposure risks, reduce downstream treatment costs, and ensure compliance with stringent biomedical waste regulations. However, most existing automated waste segregation systems have been developed for domestic or general-purpose scenarios and are poorly adapted to the operational complexity and safety requirements of hospital environments. In this study, a hospital-specific automated waste segregation system was designed, implemented, and experimentally evaluated for real-time classification of five clinically relevant waste categories: infectious waste, sharps, pharmaceutical waste, recyclable waste, and general waste. The proposed system integrates an ultrasonic sensor with a Raspberry Pi 4B platform executing a lightweight MobileNetV2 model, coupled with a motorised mechanical sorting mechanism. A curated dataset comprising 6,868 labelled hospital-waste images was constructed and used to fine-tune the model to ensure robustness under embedded deployment constraints. Experimental validation under simulated hospital disposal scenarios demonstrated an overall classification accuracy of 97%, with end-to-end segregation cycle times ranging from 8 to 12 seconds per item across repeated trials. These results indicate that high-accuracy, real-time waste classification can be achieved using low-cost embedded hardware and compact deep learning architectures. The proposed approach establishes a practical and scalable foundation for intelligent healthcare waste management at the point of disposal, offering a viable pathway toward safer clinical environments, improved operational efficiency, and the broader adoption of edge AI solutions in resource-constrained healthcare settings.