The estimation of in-situ pavement responses, such as deflections, through Non-Destructive Testing (NDT), is a widely accepted technique for assessing the strength characterization of in-service pavements. The most commonly used NDT tool is the falling weight deflectometer (FWD). The deflection profiles obtained from this NDT device are used to determine the layer moduli. Presently, various backcalculation approaches are used, and the resulting moduli vary based on the chosen methodology, algorithm, modulus range. In India, determining the optimal approach for estimating realistic pavement layer moduli is a challenging task. Hence, a comparative analysis of the different techniques employed for estimating backcalculated moduli of each pavement layer becomes imperative. This study aims to undertake such a comparison analysis. The study focuses on evaluating the variation in backcalculated moduli pavement layers using several methodologies, including the Radius of Curvature Method and the Deflection Basin Method by ELMOD (RCME, DBME), KGPBACK, approximation methods, and Artificial Neural Networks (ANN). 10 pavement stretches comprising both poor-condition and good-condition pavements were selected for field investigation and analysis. Comparing the outcomes of layer moduli among static and adaptive methods reveals that for both good and poor condition pavement stretches, the approximation method exhibits the highest percentage of variation: 29%, 20%, and 25% for surface, granular, and subgrade layers, respectively. Conversely, the ANN approach demonstrates the least percentage of variation: 9, 10, and 9% for the same layers. This study validates the suitability of the ANN method due to its robustness and simplicity.