Urban air pollution, particularly from vehicular emissions, poses a significant challenge to public health and environmental sustainability. Low-Emission Zones (LEZs) have emerged as a solution, reducing pollution in high-traffic areas by restricting access to high-emission vehicles. However, most LEZ implementations are static, failing to account for real-time changes in traffic and emissions. This review focuses on dynamic LEZ systems, which are adjusted based on real-time data to optimize emission reduction without disrupting traffic flow. By categorizing LEZ strategies into static, hybrid, and dynamic systems, this study highlights key case studies and technologies, such as traffic simulation tools and sensor networks, that enable these adaptive systems. The review also discusses the challenges and future opportunities in LEZ implementation, emphasizing the need for data-driven approaches to achieve both environmental and mobility goals. This study aims to provide insights for policymakers and researchers seeking to enhance urban air quality management through more flexible, efficient LEZ strategies.