The inertial profiler methodology is traditionally employed in RLDA (Road Load Data Acquisition) to measure road profiles and classify test routes into ISO road classes. However, this approach demands significant time and effort during instrumentation. Also, during data acquisition, laser height sensor data is affected especially during adverse conditions such as rainy seasons or on surfaces with improper reflectivity. Additionally, substantial resources are required for data processing to convert raw measurements into road classifications. To address these challenges, an initial attempt was made to establish a relationship between axle acceleration responses and road profiles, enabling axle acceleration measurements during RLDA to predict ISO road classes. However, this approach relied on a simple linear model that considered only axle acceleration responses, rendering the predictions susceptible to inaccuracies due to varying parameters such as vehicle speed. To overcome these limitations, an alternative method is introduced in this study, incorporating additional & generated parameters and employing a multiple linear regression model based on machine learning techniques. This paper outlines the detailed steps of the machine learning process, including feature engineering methods such as feature extraction, transformation, selection, and reduction. It also explores model fine-tuning strategies guided by performance metrics. The proposed methodology significantly improves the accuracy of road class predictions while reducing the time, effort, and challenges associated with instrumentation, data acquisition, and post-processing activities.