Population growth and pandemics like COVID-19 have led to the depletion of natural resources and an increase in hospital waste generation. This issue is particularly pressing in developing countries, where innovative solutions are needed to address the environmental and health risks associated with improper waste disposal. Traditional waste sorting methods, which rely on human intervention, are time-consuming and pose a significant risk of infection. Moreover, different categories of hospital waste require specific treatment methods. This study proposes an artificial intelligence-based approach for classifying and sorting hospital waste using Convolutional Neural Networks (CNNs). The proposed CNN model effectively identifies and categorizes various types of hospital waste, providing a sustainable solution that enhances regulatory compliance. The model is developed using an approach which leverages K-fold cross-validation, data augmentation, and a publicly available, modest-sized dataset tailored for variability in waste categories. The model achieved a peak classification accuracy of 97.08%, along with strong precision, recall and F1-score, despite class imbalances and the presence of visually similar and hard-to-distinguish waste categories. These results highlight the potential of the model to improve hospital waste management practices, thereby reducing environmental impact and health risks.