<p>This article systematically analyzes the evolution of AI-based traffic optimization techniques from 2019 to 2024, addressing the critical challenge of urban mobility in increasingly congested cities. While traditional traffic management methods have relied on fixed systems and basic machine learning, recent years have seen a significant shift toward advanced AI solutions including deep neural networks, generative adversarial networks (GANs), and hybrid optimization models. Through a structured five-stage methodology examining 46 research papers, this study evaluates various approaches based on accuracy, efficiency, and real-world applicability. The findings show Recurrent Neural Networks achieving 95% accuracy in traffic pattern classification and GANs reaching 98% accuracy in traffic density recognition. Hybrid models combining neural networks with optimization algorithms have demonstrated exceptional adaptability, achieving R² values of 0.999 in traffic flow prediction. Implementation of graph-based frameworks and integration of multi-modal data sources improved prediction accuracy and reduced travel times. Advancements in reinforcement and transfer learning enhance scalability, positioning AI-powered systems as key drivers of efficient and sustainable urban mobility.</p>