This chapter represents a creative approach to enhance the prediction of the Modified Structural Number (MSN) for asphalt pavements by incorporating machine learning (ML) algorithms, particularly Artificial Neural Networks (ANN), and Random Forest (RF) analysis. The study aims to predict the MSN using pavement deflections collected at radial distances of Falling Weight Deflectometer (FWD), air temperature, pavement temperature, and percentage of the cracked area as input variables. The MSN is a crucial parameter in the design, evaluation, and maintenance of flexible pavement structures, and its accurate prediction is essential for optimizing pavement management strategies. The traditional method for estimating MSN requires layer thicknesses, subgrade properties, and layer coefficients, which are destructive and time-taking procedures. This research investigates an alternative nondestructive approach using a dataset comprising 2001 data points. The dataset includes real-time pavement deflection data, temperature (air and pavement), and cracking percentages, enabling the usage of machine learning algorithms to forecast MSN values accurately. The interpretation of the above-mentioned machine learning algorithms is compared based on R-squared (R2) and Root Mean Squared Error (RMSE) metrics. The algorithm with the best results in terms of prediction accuracy, error minimization is ultimately selected as the most effective method for MSN prediction. By integrating machine learning algorithms with pavement deflection and temperature data, this study offers a nondestructive and efficient solution for predicting MSN values in asphalt pavements. From the analysis, the RF algorithm is a better prediction model with an R2 value of 0.86 at training and 0.71 at testing, respectively.