<![CDATA[Sustainable mobility is a crucial component of smart city development, aiming to create an efficient, ecofriendly, and high quality urban environment. The rapid urbanization and increasing vehicle numbers have led to traffic congestion, high carbon emissions, and inefficient public transportation systems. To address these challenges, integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies, particularly through the ESP32CAM platform, offers innovative solutions for realtime monitoring, analysis, and management of transportation systems. This study aims to develop an intelligent mobility system that leverages ESP32CAM as the primary device for capturing visual data, including traffic density, vehicle tracking, and parking area occupancy. The collected data is processed using AI algorithms to generate insights, such as traffic pattern predictions and alternative route recommendations. The gap in existing smart mobility solutions lies in their high implementation costs and limited adaptability to developing regions. this study introduces an affordable and flexible system that optimizes transportation efficiency by up to 30%, reducing travel time and carbon emissions while enhancing public transportation management. The results indicate that implementing this system significantly contributes to sustainable urban development by improving mobility, reducing environmental impact, and accelerating the transformation towards smart cities, especially in developing countries where cost-effective solutions are essential.]]>
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