This paper introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm designed for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. The MA2C algorithm incorporates a local reward system that values efficiency, safety, and passenger comfort, along with a parameter-sharing scheme to encourage inter-agent collaboration. The study leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing environmental sustainability and urban living standards. The algorithm is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The research extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the Minimizing Overall Braking Induced by Lane changes (MOBIL) models, contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.This paper introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm designed for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. The MA2C algorithm incorporates a local reward system that values efficiency, safety, and passenger comfort, along with a parameter-sharing scheme to encourage inter-agent collaboration. The study leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing environmental sustainability and urban living standards. The algorithm is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The research extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the Minimizing Overall Braking Induced by Lane changes (MOBIL) models, contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.