The purpose of this study is to investigate the efficacy of image enhancement techniques in the classification of mandibular wisdom teeth using the YOLOv8 model, a state-of-the-art deep learning framework that leverages computational intelligence and machine learning. To evaluate the impact of preprocessing X-rays on the classification accuracy of the YOLOv8 model, image enhancements (band-pass filtering, contrast stretching, binarization, and morphological operations) demonstrated a diminished accuracy of 88% compared to 94% without enhancements. The findings suggest that the original unaltered dental radiographs may provide more reliable data for machine learning models than preprocessed X-rays. This insight suggests a paradigm shift in leveraging computational intelligence to develop robust and efficient models that can process dental images with minimal preprocessing, thus optimizing clinical workflows and decision-making processes.