Abstract
Creating an advanced deep learning methodology for efficient management of electronic waste (e-waste) and preservation of environmental health is the aim of this research. The research tackles the growing problem of e-waste by compiling and preprocessing various datasets of e-waste images using a Sequential Neural Network (SNN) with TensorFlow and Keras. To improve the model's performance, this all-inclusive approach uses image augmentation techniques. In this work, efforts have been made to enhance the performance and computational efficiency of deep learning classifiers using hyperparameters. The study utilized e-waste data obtained from standard online repositories. The hyperparameter tuned modified CNN-based Sequential Neural Network model achieved an accuracy of 87%, precision of 87%, recall of 86% and f1_score of 86%. This model's strong performance highlights its real-time application potential and ease of integration into current e-waste management workflows. The suggested system is ready for widespread implementation and offers substantial advantages for environmental sustainability and resource conservation. This deep learning system helps reduce health risks associated with improper e-waste disposal, while also supporting ecological preservation by enabling the efficient sorting and classification of e-waste. The innovation resides in its capacity to streamline and automate the management of e-waste, offering a viable resolution to one of the most urgent environmental problems. This study is an excellent example of how cutting-edge artificial intelligence technologies can be integrated to improve waste management systems, giving global environmental health initiatives a scalable and useful tool.