This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm for multi-AV lane-changing in mixed-traffic scenarios, aiming to enhance urban traffic management and sustainability in smart cities. The MA2C algorithm integrates a local reward system that values efficiency, safety, and passenger comfort, along with a parameter-sharing scheme to encourage inter-agent collaboration. It leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, improving predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results show 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. The study also highlights the importance of multi-dimensional decision-making in lane-changing, considering the diverse needs of a sustainable smart city environment. The algorithm is designed to learn from experience, refining policies to achieve the best outcomes for urban sustainability and mobility. The MA2C algorithm is shown to be effective in managing traffic efficiently, particularly in less congested environments, and is proposed as a promising tool for managing urban mobility sustainably. The study also discusses the limitations of current algorithms, such as the importance of passenger comfort and the diversity of HDV behavior in real-world scenarios. The comparative analysis of the MA2C algorithm against other MARL methodologies, such as MAPPO, MADQN, and MAACKTR, highlights the strengths of MA2C in terms of adaptability and robustness in dynamic urban traffic. The study concludes that the MA2C algorithm is a promising solution for sustainable smart city transportation systems, emphasizing the potential of artificial intelligence and machine learning in developing robust, efficient, and sustainable urban mobility systems.This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm for multi-AV lane-changing in mixed-traffic scenarios, aiming to enhance urban traffic management and sustainability in smart cities. The MA2C algorithm integrates a local reward system that values efficiency, safety, and passenger comfort, along with a parameter-sharing scheme to encourage inter-agent collaboration. It leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, improving predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results show 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. The study also highlights the importance of multi-dimensional decision-making in lane-changing, considering the diverse needs of a sustainable smart city environment. The algorithm is designed to learn from experience, refining policies to achieve the best outcomes for urban sustainability and mobility. The MA2C algorithm is shown to be effective in managing traffic efficiently, particularly in less congested environments, and is proposed as a promising tool for managing urban mobility sustainably. The study also discusses the limitations of current algorithms, such as the importance of passenger comfort and the diversity of HDV behavior in real-world scenarios. The comparative analysis of the MA2C algorithm against other MARL methodologies, such as MAPPO, MADQN, and MAACKTR, highlights the strengths of MA2C in terms of adaptability and robustness in dynamic urban traffic. The study concludes that the MA2C algorithm is a promising solution for sustainable smart city transportation systems, emphasizing the potential of artificial intelligence and machine learning in developing robust, efficient, and sustainable urban mobility systems.