The rapid growth of electronic products has led to a burst of digital dumping that has caused severe environmental, technological, and regulatory tests. This paper proposes a multi-disciplinary solution that integrates machine learning (ML) algorithms with hardware-based anomaly detection systems to enhance the life-cycle assessment and predictive maintenance of E-waste. Through the use of high-end spectral analysis techniques like Pulsed laser ablation spectroscopy, in combination with supervised ML classifiers, the present method facilitates real-time detection and categorization of toxic constituents in digital dump. Moreover, the proposed framework also overcomes the issues related to the unavailability of high-quality datasets using synthetic generation of data for model training and testing. Additionally, the research discusses the role of extended producer responsibility (EPR) and other regulatory policies in encouraging sustainable E-waste management. Experimental outcomes prove that combined application of ML and hardware options effectively enhances the precision and efficacy of digital electronic waste cataloguing, risk evaluation, and environmental impact forecasting. This work adds value to the development of smart automated systems to sustainably manage E-waste.