The increasing integration of smart technologies in urban environments has led to the emergence of Smart Cities, where the Internet of Things (IoT) plays a pivotal role in enhancing the efficiency of various systems. These systems’ critical component is the Internet of Vehicular Things (IoVT), which leverages connected vehicles to optimize transportation networks. In pursuing sustainable urban mobility, this study proposes a Machine Learning (ML) based approach for optimizing green routing within the IoVT framework. The primary objective is to develop a sophisticated routing algorithm that considers real-time traffic conditions and environmental impact to minimize carbon emissions and energy consumption. Based on historical and current data, the proposed model harnesses multiple linear regression to predict carbon emission for optimized routes. The ML model dynamically adjusts routing decisions based on minimum carbon emission-enabled routes by continuously updating its knowledge, considering factors such as traffic congestion, vehicle types, and emission levels. The research contributes to the growing field of green transportation by providing a scalable and adaptable solution for optimizing vehicular routes in Smart Cities containing minimum carbon emissions. Using Multiple linear regression to predict carbon emission of a road, our model contains 82.14% accuracy. ML algorithms empower the system to make informed decisions, promoting energy-efficient and environmentally friendly transportation.