The plethora of digital interactions in today’s world necessitates the development of secure Digital Identity Management Systems (DIMS). Password-based authentication mechanisms are the predominant “something known” techniques but are susceptible to privacy leaks, internal attacks, single points of failure, phishing scams, and brute-force attacks. Using distinct and quantifiable biological traits that are “something inherent” to each individual, biometric authentication provides a far more reliable solution. The security of centralized data repositories, which store biometric templates, is vulnerable to data breach as it may jeopardize the entire system and make biometric data useless for future authentication. This chapter essentially combines the nuances of biometrics, blockchain Technology, and Machine Learning (ML) to propose, CredibleIDs, an authentication scheme that leverages the strengths of these technologies to offer a promising solution to address the limitations of traditional entity authentication methods. Blockchain technology enables a paradigm shift in data management through its immutable and tamperproof features. User privacy is protected by keeping cryptographic hashes of contents of the blockchain as opposed to actual raw data. Users can grant access to specific entities through smart contracts. The effectiveness and precision of digital identity verification procedures can be further improved by ML. Biometric characteristics can be analyzed and authenticated using machine learning techniques, offering a highly secure method of verification of digital identity. Individuals can also be identified and verified by ML algorithms using their behavioral traits. Through the application of machine learning, abnormal patterns in user behavior can be found, facilitating quick identification of suspicious activity. The adoption of multi-factor authentication, combining various authentication methods, can create additional layers of security.