The rapid growth of cities leads to three main problems which affect transportation performance along with system defense and infrastructure dependability. The traditional urban planning methods became less efficient because of rising populations and improved road volumes and growing energy requirements. The study creates an entire machine learning system which unites traffic management techniques with environmental solutions to generate efficient energy consumption and durable solutions for negative outcomes. The system enables instant decision-making about urban growth through its data processing capabilities which upholds organizational goals for developing technology alongside environmental protection objectives. The main component of this study uses real-time traffic data to enable city officials for strategic planning through a sustainability measurement framework. The analysis emerged with a 5.0% drop in emissions together with a 3.0% drop in energy consumption and created a 45.0% increase in green infrastructure during the analysis period from January through June 2025. Research data indicates urban sustainability progresses because machine learning systems analyze environmental factors and traffic data which leads to better community way of living. Flexible artificial intelligence models enable continuous urban planning system progression leading to the creation of smarter ecological urban districts with greater reliability in their development. The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words.