The intricate relationship between recycling practices and the generation of solid waste emphasizes the multifaceted character of sustainable waste management. A comprehensive comprehension of these dynamics is crucial for formulating specific interventions and policies that optimize the positive effects of recycling on our environment. This research explores the application of deep learning, specifically utilizing TensorFlow, in the domain of waste classification for enhanced waste recycling. The study focuses on the efficient categorization of diverse waste materials, including cardboard, glass, trash, metal, and plastic, through the implementation of advanced neural networks. By leveraging TensorFlow's capabilities, our research demonstrates the successful development and deployment of deep learning models that exhibit accurate and reliable waste classification. The utilization of deep learning model was able to select accurately the most important types of garbage worthy for recycling processes. And not only streamlines recycling processes but also addresses the environmental challenges associated with inefficient waste management. The findings finally highlight the transformative potential of integrating cutting-edge technologies into waste sorting systems, paving the way for a more sustainable and eco-friendly approach to waste disposal. This research contributes to the ongoing efforts in environmental conservation by presenting a viable solution to enhance waste recycling practices through the power of deep learning.