Rapid urbanization with population growth leads to a sharp increase in waste generation, which causes significant environmental damage. Despite the daunting nature of this challenge, it can be effectively managed by promoting waste recovery. In our research, we propose a new approach that uses machine learning and blockchain technologies to solve this problem. Our system uses a Deep Neural Network (DNN) based on the Efficientnetb0 architecture, which is trained on about 11,700 images and achieves a training accuracy of 94% in identifying garbage objects. We have developed a complete solution where DNN detects garbage objects and makes suggestions. several do-it-yourself (DIY) ideas to reuse or recycle them. To ensure transparency and make decision- making more efficient, all transactions are recorded in a blockchain ledger that allows verification and validation of DIY ideas proposed by network participants. The integration of smart contracts with the Ethereum blockchain platform further improves the reliability and security of our system. Our template is accessible through a user-friendly web platform built with Streamlit, which includes a web-capture script written in Python that effectively sources DIY ideas. . Scraping the web typically takes about a second on a desktop computer running Ubuntu 18.04 64-bit with an Intel Core i7 processor, 16GB of RAM, and Python 3.6.In addition, we perform a blockchain performance evaluation. based on smart contracts that evaluate latency and performance using Ethereum benchmarking tools. To our knowledge, our research is a pioneering effort to integrate blockchain technology and deep learning to develop a DIY recommendation system to address waste management challenges.