An Integrated Machine Learning and Blockchain Framework for E-Waste Forecasting and Traceability

Abstract: One gadget after another piles up worldwide, piling pressure on nature’s limits - trash from old phones and laptops might hit 80 million tons by 2030. Behind the scenes, today’s recycling networks struggle: they’re run by too few hands, hard to track fully, open to cheating or altered logs. Tackling these two issues at once, a new system links smart forecasts with tamper-proof digital records. Numbers pulled from past trends between 2010 and 2023 feed into a math model; things like people count, money per person, online access help guess future waste amounts. From those patterns, predictions stretch ahead - from 2026 through 2030 - showing where trash tides may rise. One way to track old electronics uses Python, building a chain of records locked with SHA-256 math plus agreement through computing effort. Instead of paper logs, it runs on a live web interface made with Streamlit, storing data in MySQL. Tests show predictions stay close to real outcomes - over 96 percent match by one measure. This setup links smart forecasts to verified tracking, fitting global targets for responsible tech, cleaner production, and climate action.

Keywords: E-Waste, machine learning, blockchain, linear regression, SHA-256, proof of work, forecasting, traceability, streamlit, sustainability.