The concept of Kullback-Leibler Divergence (KL Divergence) can be applied across various fields beyond its traditional use in probability theory and information theory while maintaining its essence as a measure of dissimilarity between two probability distributions. In machine learning, KL Divergence is commonly used for regularization purposes when training Bayesian neural networks or optimizing generative models like variational autoencoders. In natural language processing, KL Divergence has been employed for topic modeling tasks such as comparing document similarity based on word distributions. Furthermore, in image processing applications like image compression or reconstruction algorithms, KL Divergence serves as a metric for evaluating the difference between original images and compressed representations based on pixel intensities or features.