Style Mapping is a computational technique applied within the field of Natural Language Processing (NLP) to convert text from one style to another while preserving its semantic content. This process involves intricate algorithms and machine learning models that identify and understand stylistic nuances, subsequently transforming them into a chosen style. The concept garners interest across various fields, including creative literature, journalism, and marketing due to its potential in enhancing written communication by tailoring it to specific audiences or contexts. Furthermore, it plays an instrumental role in sentiment analysis and linguistic transformation tasks within artificial intelligence applications. Beyond textual data, style mapping is also employed in fashion industry for predicting trends and designing new collections by considering historical patterns and contemporary influences. It represents an exciting intersection of language science, artificial intelligence, and design principles.