This research paper provides an extensive overview of machine learning techniques for classification and regression tasks. The study includes a rigorous experimental evaluation conducted on the Vinho Verde dataset. The classification methods investigated in this research encompass the k-nearest-neighbor classifier, boosted decision trees, and support vector machines. The study explores multivariate linear regression and extreme learning machines for regression analysis. A comprehensive theoretical examination of these methods and detailed explanations of the implementation steps for the k-NN classifier, multivariate linear regression, and extreme learning machine algorithms are presented. Notably, the bagged decision trees classifier demonstrated the highest performance among the classification methods, while the extreme learning machine exhibited superior results for regression tasks. The findings of this research contribute valuable insights into the effectiveness of these machine learning techniques, opening avenues for further exploration and application in real-world scenarios.