The improper disposal and mismanagement of medical waste pose severe environmental and public health risks, contributing to green- house gas emissions and the spread of infectious diseases. Accurate medical waste classification is crucial for mitigation. We explore integrating capsule networks with a pretrained DenseNet model to improve medical waste classification – marking the first application of capsule networks in this domain. A diverse dataset of medical waste images, collected from multiple public sources, is used to evaluate three model configurations: 1) a pretrained DenseNet model as a baseline, 2) a pretrained DenseNet with frozen layers combined with a capsule network, and 3) a pretrained DenseNet with unfrozen layers combined with a capsule network. Results demonstrate that incorporating capsule networks improves classification performance, with F1 scores increasing from 0.89 (baseline) to 0.92 (hybrid model with unfrozen layers). This highlights their potential to address the spatial limitations in convolutional models and improve classification robustness. While the capsule-enhanced model demonstrated improved classification performance, direct comparisons with prior studies were challenging due to dataset differences. Previous studies relied on smaller, domain-specific datasets, which inherently yielded higher accuracy. Our study employs a significantly larger and more diverse dataset, leading to better generalization but introducing additional classification challenges. Future research should explore fine-tuning model architectures, integrating additional data augmentation techniques, and evaluating the model on standardized benchmark datasets. These efforts will be essential to further optimize the application of capsule networks in medical waste classification and enhance their real-world applicability in automated waste management systems.