In the domain of biometric systems, where individual identities are confirmed through distinctive traits like fingerprints, facial geometry, and gait patterns, the application of supervised and unsupervised machine learning techniques plays a pivotal role. This chapter provides a comprehensive examination of these approaches within the context of biometric systems. Supervised learning, which relies on labeled data to train models for predicting outcomes, has proven effective in various biometric applications, employing algorithms such as Convolutional Neural Networks, Support Vector Machines, logistic regression, and Decision trees. Unsupervised learning, in contrast, excels in automatic feature extraction, data analysis, and learning strategy creation. While it may not be the primary choice for identification, it contributes significantly to improved feature fusion and data analysis. This chapter offers a detailed exploration of these machine learning techniques, assessing their suitability for both identification and verification processes. Furthermore, it addresses the persistent challenges faced in biometric system development, ranging from handling numerous identities and security concerns to extracting relevant data from noisy inputs. Privacy, data breaches, and the evolving nature of biometric attributes are also discussed. With biometrics increasingly integrated into everyday devices like smartphones, this chapter underscores the balance required between security and usability, exploring the motivations driving enhancements in biometric recognition methods to meet the growing demands for performance, usability, and security. Additionally, the chapter provides a comprehensive overview of various biometric techniques, highlighting their respective advantages and challenges, thereby offering insights into their uniqueness and application suitability. In summary, this chapter serves as an invaluable resource for those involved in the dynamic and ever-evolving field of biometrics.