This study addresses the inefficiencies in how idle taxis determine their cruising strategy, which currently rely heavily on drivers’ personal experiences. Such reliance often leads to inefficient roaming and delays in service. We propose a novel strategy for managing a small scale taxi-fleet, which is common for alliance business, utilizing real-time data and multi-agent reinforcement learning to predict potential travel demands and strategically direct idle taxis to high-demand zones. This method aims to optimize the utilization of idle taxis, reduce passenger wait times, and enhance the ride-hailing ecosystem’s overall efficiency. Our approach integrates a spatial-temporal service request model with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It innovates further by employing varied reward mechanisms informed by actual order data and the fluctuating of supply and demand, which does not only enhance the training efficiency in complex scenarios but also captures the dynamics across the service area, effectively managing supply-demand imbalances. Experimental results demonstrate that our strategy significantly outperforms several state-of-the-art methods, representing a major advancement in the optimization of transportation services. This study not only provides a more efficient framework for managing idle taxi fleets but also offers insightful implications for the future enhancement of the transportation sector.