Smart E-Waste Recycling Using AI and Blockchain: Enabling Sustainable Resource Recovery for Sustainable Power Solutions

The rapid increase of electronic waste (e-waste) poses severe environmental and health risks. This paper proposes a hybrid framework integrating deep learning, reinforcement learning, blockchain, and IoT for automated e-waste classification, optimized disassembly, and tamper-proof traceability. A ResNet-50 classifier trained on the Kaggle E-Waste Image Dataset achieved 93.7% classification accuracy and an F1 score of 0.92. A Q-learning agent optimized dismantling routes to prioritize high-value, low-toxicity components, improving material recovery in simulation. A private Hyperledger Besu deployment delivered an average block time of ≈5.3 s, smart-contract execution time of ≈2.1 s, and 99.5% uptime, enabling tokenized asset tracking (4,200+ tokens). Lifecycle analysis indicates up to 30% carbon-emission reduction versus traditional methods and improved recovery of lithium, cobalt, and rare-earth elements for renewable energy applications. The paper demonstrates measurable environmental and economic benefits and outlines limitations and directions toward field deployment.