To address the issues that multi-axle special vehicles are prone to rollover accidents during high-speed maneuvers due to their high load capacity and elevated center of mass (CoM), and that the traditional Time-To-Rollover (TTR) rollover early warning algorithm based on state estimation suffers from misjudgment and false alarm problems, this study takes a specific five-axle special vehicle as the research object to investigate rollover early warning algorithms. First, a vehicle dynamic rollover index accounting for unsprung mass was established, the index was validated via multi-condition simulations, and a rollover early warning threshold of 0.8 was determined in conjunction with the dynamic characteristics of multi-axle special vehicles. Subsequently, integrating vehicle state estimation results, a TTR rollover early warning algorithm based on state estimation was developed, and its early warning performance as well as inherent limitations were verified via simulations. Finally, a neural network-corrected TTR rollover early warning algorithm (NN-C-TTR) was proposed, the overall architecture of the algorithm and the neural network module were designed, and the algorithm was validated through both simulation tests and real-vehicle tests. The results demonstrate that the proposed algorithm effectively addresses the misjudgment and false alarm issues of the traditional TTR early warning algorithm, achieves accurate advance warning of rollover risks for multi-axle special vehicles, and exhibits outstanding reliability and engineering practicality.