The rapid pace of urbanization and population growth has led to a significant increase in solid waste generation, posing substantial challenges for conventional waste management systems. In response, the Internet of Things (IoT) offers a transformative solution through the development of Smart Garbage Bin (SGB) systems. This study focuses on the design and implementation of an IoT-enabled SGB mechanism aimed at optimizing waste collection and management. The proposed system integrates advanced sensors within garbage bins to enable real-time monitoring and measurement of waste volume. These sensors utilize technologies such as ultrasonic, infrared, and motion detection to accurately assess fill levels. The collected data is then transmitted wirelessly to a centralized cloud platform using communication protocols like LoRaWAN or NB-IoT. Within this cloud-based environment, sophisticated data analytics and machine learning algorithms process the incoming data, enabling optimized decision-making regarding waste collection schedules and routes. By dynamically allocating resources based on real-time fill levels, the system improves collection efficiency while reducing operational costs. Additionally, the SGB system offers environmental benefits, such as reduced carbon emissions through optimized routes, improved urban cleanliness, and enhanced sustainability in waste management practices. The practical implementation of the system in an urban setting demonstrates its effectiveness and feasibility, with evaluations covering aspects such as system reliability, data accuracy, energy efficiency, and user interface design. The results indicate that IoT-enabled SGBs have the potential to revolutionize traditional waste management practices, fostering more efficient, data-driven, and environmentally sustainable approaches. This system could play a pivotal role in advancing smart city initiatives and improving urban living standards through optimized waste management strategies.