"The increasing adoption of autonomous vehicles (AVs) presents significant challenges in managing intersections efficiently while minimizing conflicts. Traditional centralized traffic control systems struggle with scalability and adaptability, whereas decentralized approaches often face coordination and safety issues. Ensuring real-time decision-making and cooperation among AVs in dynamic, multi-agent environments remains a critical challenge. This research investigates a Multi-Agent Deep Reinforcement Learning (MADRL) framework for Emergent Cooperation and Conflict Resolution in AVs. The approach utilizes decentralized learning, allowing AVs to develop cooperative driving strategies based on observed behaviors and shared environmental interactions, rather than explicit communication. The proposed MADRL model is evaluated against a baseline algorithm, MADQN-AV, using key performance metrics, including collision rate, throughput, average delay, and decision accuracy. Experimental results indicate that the MADQN framework significantly reduces collision rates, enhances traffic efficiency, and maintains real-time decision-making capabilities with minimal computational overhead. Comparative analysis with MADQN-AV demonstrates superior scalability and adaptability, validating the potential of decentralized multi-agent learning in autonomous traffic systems."