<p>Recent years have seen remarkable progress in the integration of Density Functional Theory (DFT) calculations and Machine Learning (ML) techniques, particularly within the realm of materials science. This review delves into this burgeoning field, focusing on their application to MXenes, a significant family of two-dimensional (2D) materials comprising transition metal carbides, nitrides, or carbonitrides. MXenes have garnered attention for their remarkable properties and versatile applications, including catalysis, gas sensing, and energy storage. We further investigate the integration of ML methodologies, utilizing various algorithms to predict MXene properties with high accuracy and efficiency. Through a comprehensive literature analysis, we underscore the synergistic relationship between DFT calculations and ML approaches, showcasing how their combined use accelerates MXene discovery and optimization for diverse technological applications. Additionally, we discuss current challenges and future directions in leveraging these computational techniques to advance our understanding and utilization of MXene materials. This review serves as a valuable resource for researchers at the intersection of computational methods and materials science, offering insights into state-of-the-art techniques for predicting MXene properties, performance, and guiding future research endeavors in this dynamic field.</p>