Machine learning (ML) is the backbone of modern data-driven decision systems. Two primary paradigms — supervised and unsupervised learning — provide complementary strategies for extracting value from labeled and unlabeled datasets respectively. This paper presents a comprehensive comparative study of supervised and unsupervised models, including theoretical background, literature synthesis, experimental evaluation on benchmark datasets (Iris and a MNIST subset), practical applications, ethical considerations, and recommendations for hybrid integration. Results indicate that supervised models consistently yield higher predictive performance when good-quality labels are available, while unsupervised techniques reveal structural insights and are indispensable for exploratory analysis and representation learning. The paper concludes with future research directions emphasizing semi/self-supervised approaches, interpretability, and ethical deployment.