In machine learning and medical data analysis, feature selection is crucial because it helps find the most important characteristics while removing those that are unnecessary or redundant. Many features in datasets used in medical applications may add noise or complex computations, and many of these features do not substantially improve predictions. This chapter explores various feature selection techniques, broadly categorized into supervised and unsupervised methods. Supervised methods for feature selection rely on annotated data to determine the importance of features. These include filter, wrapper, and embedded methods. Filter methods evaluate the significance of each feature separately using statistical tests such as Chi-square, ANOVA, and Mutual Information. These methods are computationally efficient and scalable, but do not consider feature interactions. Wrapper methods, such as Recursive Feature Elimination (RFE) and Sequential Feature Selection, evaluate feature subsets by training a predictive model, leading to improved accuracy but higher computational costs. Embedded methods, including regularization techniques like Lasso and Ridge regression, perform feature selection as part of the model training process, balancing accuracy and efficiency. In contrast, unsupervised feature selection methods do not require labeled data and rely on data dimension criteria to reduce the number of features. These methods are particularly useful in exploratory data analysis and clustering applications. By utilizing suitable feature selection techniques, researchers can improve model performance, enhance interpretability, and reduce overfitting. This chapter explores the benefits and drawbacks of each method, with a focus on their applications in medical research, including genomic analysis, medical imaging, and electronic health records.