The rapid increase in urbanization and vehicular growth has led to significant traffic congestion challenges, which traditional traffic management systems struggle to address. Intelligent traffic management supported by big data and advanced analytics offers a promising solution by creating adaptive and efficient systems to manage urban traffic. Big data, sourced from GPS devices, sensors, and public transport systems, provides real-time insights into traffic dynamics, enabling data-driven decision-making. The process begins with data collection and integration, ensuring data accuracy and consistency. Scalable storage solutions, like cloud computing, handle large volumes of data. Predictive analytics, leveraging machine learning models, forecasts future traffic conditions, allowing for proactive management. Prescriptive analytics optimizes traffic flow, utilizing algorithms to recommend the best actions. Advanced techniques, including real-time monitoring, dynamic signal control, and incident detection, enhance traffic efficiency. Predictive tools also support long-term planning and infrastructure development, while optimizing public transport routes and schedules. Emerging technologies like AI and IoT additional recover these systems, but policy frameworks and stakeholder collaboration are essential for successful implementation. This integration of big data and analytics marks a transformative step toward smarter, more efficient traffic management.