The fast growth of urban populations and the rising complexity of city surroundings have presented serious waste management problems particularly in terms of guaranteeing efficiency, sustainability, and environmental preservation. Enhanced Waste Management Systems (EWMS) for Smart Cities seek to solve these issues by combining modern technologies including the Internet of Things (IoT), big data analytics, machine learning, and automation, by means of better trash collecting, segregation, recycling, and disposal techniques, these systems help to manage municipal waste. Smart bins embedded with sensors can track the waste levels in real time and therefore generate data that supports better waste collecting plans, optimized routes, and less fuel usage that all lead to reduced greenhouse gas emissions. Furthermore, predictive algorithms and data analytics help to foresee waste generating tendencies, therefore enabling proactive management and quick reactions to waste-related problems. Artificial intelligence-powered advanced sorting systems help to better separate waste at the source, therefore increasing recycling rates and lowering the amount of waste transported to landfills. Moreover, clever waste management systems fit perfectly with other smart city infrastructure, building a coherent ecosystem for urban growth emphasizing sustainability, resource economy, and enhancement of city dwellers’ quality of life. This work investigates the main elements, advantages, and difficulties of adopting such systems, therefore illustrating how a smart, data-driven waste management approach might help to create more resilient, cleaner, greener cities.