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Electronic waste generation is increasing dramatically throughout the world. Consequently, this increase in E-waste harms the environment, health, and other aspects of human life. Moreover, hazardous substances and the informal disposal of E-waste severely threaten human health and the environment. Saudi Arabia is the largest Arab country in terms of electronic waste generation and is the Arab country that generates the most E-waste. Over the past few decades, several initiatives and policy implementations have been undertaken in the country. However, the management of E-waste is still a source of distress and an unresolved issue. Sustainable development requires much more effort, primarily efficient E-waste management, which can only be achieved by establishing a formal collection system, early forecasting, and accurate estimations. The purpose of this study is to provide an overview of the fundamental and emerging trends in E-waste production worldwide and in Saudi Arabia. This paper aims to summarize the hazardous elements present in E-waste, their dangerous effects, and the economic potential for recycling E-waste as a means of developing sustainable communities. This study explores the practices regarding efficient E-waste management and E-waste estimation and prediction globally, specifically in Saudi Arabia and other Arab countries. This study suggests that more than the use of a single management probe will be needed to achieve significant results. Instead, a complementary approach can be used to achieve the most effective results. Additionally, people should be aware of the importance of handling and recycling E-waste. This study emphasizes the importance of formal collection and documentation to ensure effective monitoring and sustainable development in any developed country.

Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods.