The intelligent cooling system serves as a critical subsystem for autonomous mining dump trucks, playing a pivotal role in ensuring thermal safety under complex operating conditions. However, existing cooling systems for mining dump trucks lack adaptive capabilities to simultaneously handle engine load fluctuations and extreme environmental conditions (−40 ℃ to 45 ℃). To address this gap, this study presents a comprehensive automated cooling system design integrating intelligent sensing, fuzzy PID control, and remote monitoring capabilities for a heavy-duty mining dump truck, based on forced circulation water cooling architecture. The system employs multi-source sensors to acquire real-time engine thermal load data, which is processed through a fuzzy PID algorithm to dynamically regulate fan speed and coolant pump flow rate, thereby achieving adaptive optimization of heat dissipation efficiency. EPQ testing results demonstrate robust fundamental cooling performance, with the system exhibiting a 20% reduction in coolant temperature stabilization time during simulated autonomous operation dynamic load tests, remote monitoring latency ≤35 ms, and fault prediction accuracy reaching 98.5%. This solution not only fulfills essential engine cooling requirements but also establishes a reliable thermal management framework to support automated mining truck operations.