The suspension dampers play a pivotal role in controlling suspension dynamics, especially during rapid suspension movements such as pothole impacts, speed bumps, and off-road conditions. The dampers directly influence the ride comfort, vehicle stability. Ensuring the durability of these valves is essential to maintain consistent performance throughout the vehicle's lifecycle. To validate the valve configuration at high speed, in current development practices, durability cycles are typically derived through physical data collection on actual vehicle driven on proven ground tracks. While this method provides accurate real world validation, it is highly time consuming and resource intensive. The dependency on full vehicle testing also limits early phase of validation, creating bottlenecks in the product development cycle. An AIML based methodology is presented in this paper to predict the HSVD cycles for damper validation. The AIML algorithm was trained using the historical test data and identified vehicle and damper design parameters. The critical design parameters used includes damping forces in compression & rebound condition at different velocities, damper stroke , spring rate, bump stopper stiffness, sprung & un-sprung mass, FAW , tyre size etc. Random Forest machine learning algorithm was used to develop the AIML model. Out of the historical test data set, 80 % test data was used for training the AIML algorithm and remaining 20% was used for validation purpose. The paper presents the step-by-step process used for development and validation of the AIML model. The developed AIML models achieved > 80% prediction accuracy with physically test data. The accuracy of the model can be further enhanced with availability of new test data points. This approach will be useful in early validation of dampers without dependency on vehicle level testing.