Numerous ecosystems are under threat from plastic waste and carbon emissions. One proposed solution involves the use of biodegradable plastics, which are designed to break down quickly through microbial assimilation, thus mitigating climate change, microplastic pollution, and littering. Nonetheless, biodegradable plastics make up only a small percentage of the global plastics market and necessitate additional research and commercialization endeavors. A range of considerations must be taken into account, including the environmental and socio-economic consequences, governmental policies, standards and certifications, physico-chemical attributes, and analytical techniques. When non-biodegradable and toxic substances are involved, improper waste disposal may result in increased pollution, global warming, and health hazards, underscoring the importance of appropriate waste disposal methods. One innovative approach to streamlining waste classification involves deep learning and convolutional neural networks (CNNs). An image dataset is utilized to train the CNN, and several visualization methods are employed to distinguish waste types based on color and texture. The model is deployed via the Django web framework. Collectively, these efforts demonstrate the potential of advanced technology to address environmental challenges.