The growing complexity of urban mobility, coupled with escalating environmental concerns, has prompted the need for innovative solutions to optimize traffic management. Intelligent Transportation Systems (ITS) have emerged as a transformative framework designed to enhance urban transportation efficiency, safety, and sustainability. This review investigates the integration of route optimization algorithms and environmental data within ITS, focusing on how these technologies can address the dual challenges of congestion and pollution in urban areas. The synergy between advanced algorithms and real-time environmental data, such as air quality, weather conditions, and noise pollution, can significantly improve traffic flow, reduce fuel consumption, and lower carbon emissions. The integration of machine learning (ML) and artificial intelligence (AI) further enhances the adaptive capabilities of ITS by predicting traffic patterns, optimizing routes, and mitigating environmental impacts. Additionally, the rise of smart cities powered by 5G and Internet of Things (IoT) technologies offers vast potential for more responsive and sustainable urban mobility systems. Despite its promising capabilities, the widespread implementation of ITS encounters challenges. These includes data accuracy, real-time processing, and privacy concerns. To overcome these obstacles, it is recommended that there should be an improve sensor technology and integrate data across various sources such as IoT, satellite imagery, and social media. Additionally, leveraging AI and machine learning for dynamic route optimization can lead to more efficient transportation networks. Investing in smart city infrastructure and promoting public-private partnerships for data sharing will help address regulatory and privacy issues. These steps will ensure the scalability and sustainability of ITS, driving the development of smarter and greener cities