Safety is a fundamental aspect of the nuclear industry, particularly in the aftermath of the Chernobyl and Fukushima accidents. One of the most critical phenomena affecting reactor safety is the Critical Heat Flux (CHF), which marks the transition to a deteriorated heat transfer regime in nuclear reactor cores, potentially leading to severe damage. Accurate CHF prediction is therefore crucial for enhancing nuclear safety and reactor performance. Since 2006, CHF prediction has largely relied on the Look-Up Table (LUT) method, a well-established empirical approach. However, with advancements in computational techniques, machine learning (ML) has emerged as a promising tool to improve prediction accuracy.