The rapid development of connected car technology imposes vital requirements to develop adaptive and robust intelligent computational models to help enhance communication, safety, and autonomous decision-making for vehicles. This paper deals with how to incorporate neuro-evolutionary computing-the hybrid paradigm of neural networks and evolutionary algorithms-into the connected car ecosystem. We thus propose a holistic framework that harnesses the adaptive learning capabilities of neural networks and the optimization strengths of evolutionary algorithms to ensure optimal real-time decision-making processes for efficient traffic management and safe passage by passengers. The framework houses a multi-agent system, wherein every connected vehicle is an intelligent agent in itself-learning and evolving toward dynamic traffic surroundings. We present simulations and experimental results showing the effectiveness of our neuro-evolutionary approach to reduce communications latencies and lead to better optimization of routes through proactive collision avoidance. From these results, it is suggested that there exists significant promise for neuro-evolutionary computing to address computational challenges in connected car networks, paving the way to safe, resilient, and efficient intelligent transportation systems.