The growing dependence on online learning platforms has intensified concerns about the authenticity of academic credentials, the protection and privacy of student data, and the reliability of mechanisms used to evaluate student performance. Conventional checking procedures are time-consuming and manual, and may be vulnerable to forgery or tampering of documents. Similarly, existing academic analytics systems often rely on data centralization processing with many concerns with respect to privacy and interoperability in (different) institutions. This paper provides a comprehensive review of recent technologies and architectural trends for building secure, open and smart academic systems. The survey covers all major methodologies for verifying academic certificates, including block chain verification, public key infrastructure (PKI), digital verifiable signatures, QR code-based verification, digital watermarking VCs, DIDs, ZKPs, SMPC, HE, cloud- based validation and edge-enabled distributed frameworks. Such studies also suffer from lack of verification mechanisms, and the corresponding modern privacy-preserving learning techniques are examined in this paper; it is however evident that integrated distributed mechanisms have to improve further the capability of dealing with credentials as well as enabling performance prediction. The findings highlight the importance of integrated solutions for secure credential storage, reduced verification time and improved institutional decision-making in evolving digital education environments.