X-Ray fluorescence spectroscopy is a powerful analytical technique widely used for elemental analysis in various matrices, including food products. The complexity of food matrices and the need for precise quantification require advanced data analysis techniques, leading to the integration of XRF with chemometrics. Chemometrics, which involves the application of mathematical and statistical methods to chemical data, enhances the interpretative power of XRF by improving accuracy, precision, and sensitivity in detecting and quantifying elements in food products. This review explores the principles and applications of XRF Spectroscopy in food analysis, emphasizing its advantages and limitations. It delves into the fundamental aspects of chemometrics, including principal component analysis, partial least squares, and multiple linear regression, which are commonly employed to process and interpret complex spectral data. By coupling XRF with chemometric methods, researchers can overcome challenges such as matrix effects, spectral overlaps, and interference, leading to more robust and reliable analytical results. The application of XRF in food analysis is examined through case studies that demonstrate its effectiveness in detecting elements like heavy metals, minerals, and trace elements in various food products. These studies highlight the potential of XRF in compliance with regulatory standards and ensuring consumer safety.