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Traffic congestion is a pervasive and escalating global challenge, particularly in dense urban areas, leading to significant economic, social, and environmental costs. Traditional mitigation strategies are proving insufficient, highlighting the need for a new approach. This report focuses on extreme congestion, which represents a critical breakdown in network efficiency and demands innovative solutions. The study defines and quantifies extreme congestion, moving beyond simple speed metrics to include reliability concepts like the Planning Time Index and Buffer Time Index. It explores advanced theoretical frameworks, such as Kerner's Three-Phase Traffic Theory, and reviews various traffic modeling approaches, including macroscopic, microscopic, and mesoscopic models. The increasing importance of Machine Learning and Deep Learning for real-world applications and real-time operations is emphasized, with mesoscopic models highlighted for their balance of detail and computational efficiency. The report also examines the dual potential of autonomous vehicles (AVs). While AVs offer promise for alleviating congestion through improved capacity and optimized flow, challenges like induced demand and complex human-AV interactions could exacerbate it. The actual impact will depend on factors like AV penetration rates and human driving behavior. Ultimately, managing extreme congestion in the AV era requires a fundamental shift towards proactive, predictive, and collaborative traffic management systems. This involves leveraging AV capabilities through adaptive traffic signal control, smart rerouting, and platooning, supported by Vehicle-to-Everything (V2X) communication and integrated smart city infrastructure. Policy interventions, such as dedicated lanes and congestion pricing, will also be crucial. The report concludes that a holistic approach, prioritizing collaborative autonomous systems, addressing human factors, and thoughtful urban planning, is essential for creating truly efficient, safe, and sustainable transportation networks.