An electronic waste (e-waste) has become major issue in worldwide due to its rapid growth in environmental and public health. This paper explores about artificial intelligence (AI) that provide in e-waste solutions to transform waste management systems. A new AI-based paradigms is applied as novel AI architectures to assess how machine learning algorithms and predictive analytics can progress the performance of e-waste collection through sorting, recycling and recovery operations. The multi-modal CNN-LSTM hybrid model developed in the study provided an accuracy of 97.3% on e- waste component classification, while our reinforcement learning based disassembly pathway optimization system reduced process time by 42% compared to conventional approaches. The developed intelligent system was installed in a pilot plant where the recovery of valuable materials from waste increased by 28.6% and exposure to hazardous materials was cut by 73.4%. The paper also highlights challenges such as lack of data, technological infrastructural needs and regulatory concerns. We then use our findings to develop a framework for AI diffusion into e-waste management systems and suggest policy inputs to foster widespread adoption.