Real-time traffic management has become the backbone of modern urban planning, with AI systems at the forefront of optimizing traffic flow and reducing congestion. With urbanization fast-paced worldwide, cities face unprecedented traffic challenges, such as increasing vehicle density, unpredictable congestion patterns, and growing environmental concerns. This paper reviews the AI-based traffic management system implemented in Singapore, a global leader in smart city innovation. Advanced techniques of AI are put forward by Singapore's LTA in managing traffic such that intelligent traffic lights come through predictive analytics, amongst its integration with public transportation means, cutting significant congestion, travel time as well as vehicle emissions levels alongside improving road safety all across. The potential applicability of such systems is also discussed in Indian cities like Mumbai and Bengaluru. With high population density, diverse traffic compositions, and infrastructure constraints, cities are more demanding and require innovative solutions to handle these challenges. AI-based traffic management could be applied to adjust the timing of traffic signals dynamically, optimize public transport efficiency, and reduce emergency response times for transformative changes in urban mobility. However, fragmented data systems, infrastructure limitations, and cost barriers pose huge implementation challenges in India. By comparing Singapore's success to the realities of Indian cities, this research highlights what's needed to adapt and scale AI technologies to meet local needs. It concludes that the integration of AI-driven systems can provide Indian cities with a sustainable path forward regarding traffic congestion, reduced environmental impact, and the quality of life in an urban setting.