‎With the advancement of biological sciences and modern technologies‎, ‎high-dimensional data‎, ‎in which the number of independent variables exceeds the number of observations‎, ‎have become increasingly prevalent in various fields‎. ‎Gene expression data associated with riboflavin or vitamin $B_2$ production represent an important example of such datasets‎. ‎Riboflavin‎, ‎as an essential micronutrient‎, ‎plays a crucial role in metabolic reactions and vital biological processes through its coenzyme forms‎, ‎$mathit{FAD}$ and $mathit{FMN}$‎. ‎Therefore‎, ‎the biological production of this vitamin using microorganisms as a sustainable and economical approach has gained considerable importance in the food‎, ‎pharmaceutical‎, ‎and animal feed industries‎. ‎The presence of a large number of variables and multicollinearity among them presents major obstacles to classical regression methods‎. ‎Consequently‎, ‎regularisation and dimensionality reduction approaches‎, ‎including Ridge regression‎, ‎Lasso‎, ‎Elastic Net and Partial Least Squares (PLS)‎, ‎have been developed to improve model stability and predictive performance‎. ‎Moreover‎, ‎machine learning methods such as Support Vector Machines (SVM) and Support Vector Regression (SVR) have been widely applied in high-dimensional data analysis due to their ability to capture complex and nonlinear relationships‎. ‎In this study‎, ‎regression and machine learning methods‎, ‎including Ridge‎, ‎Lasso‎, ‎Elastic Net‎, ‎PLS‎, ‎SVM‎, ‎and SVR‎, ‎are implemented and compared using real riboflavin production data and simulated datasets to evaluate their performance in handling multicollinearity‎, ‎dimensionality reduction‎, ‎and prediction of production levels.}