In the domain of Machine Learning (ML), models are celebrated for their high accuracy, however, integrating them into resource-constrained embedded systems poses a formidable challenge. This study empirically demonstrates that traditional magnitude-based pruning techniques, though effective in compressing model size, lead to underfitting, reducing the model's ability to discern complex features. Additionally, the compression-to-accuracy ratio of eXplainable Artificial Intelligence (XAI) pruning techniques is explored. The research postulates that leveraging XAI techniques in model pruning achieves higher compression rates than conventional magnitude-based methods without inducing underfitting. XAI pruning removes redundant neuron groups, preserving the overall "knowledge." Examining ResNet50 and VGG19 models on CIFAR-10 data, the study compares magnitude-based and XAI pruning methods across varying pruning targets and rates. Our results confirm underfitting with magnitude-based pruning and validate XAI's superiority in retaining accuracy during compression. The second experiment focuses on the changes in XAI features during pruning, emphasizing the reliability of XAI pruning over magnitude pruning. In conclusion, this study underscores the value of XAI pruning over magnitude pruning in retaining model accuracy. Results reveal that XAI-driven pruning is a viable solution for reducing ML model parameters in resource-constrained environments, ensuring accuracy is retained while mitigating the impact of model size reduction.