Cloud computing has become an essential platform for business data storage and application management due to its scalability, accessibility, and cost-effectiveness. However, ensuring the security and privacy of sensitive cloud data remains a major challenge because cloud users do not have direct control over their stored information. Traditional single-biometric encryption approaches often suffer from issues such as biometric variability, spoofing risks, and single-point failure. To address these limitations, this paper proposes a multi-user multi-modal biometric encryption framework integrated with threshold-based access control for securing business data stored in cloud environments. In the proposed approach, fingerprint and audio biometric modalities are pre-processed to extract privacy-preserving feature vectors, which are fused to generate individual user-keys using a Hash-Based Key Derivation Function (HKDF). Subsequently, a master encryption key is generated through Shamir’s Secret Sharing and Lagrange interpolation mechanisms, where only a predefined threshold number of valid user-keys can reconstruct the master key for decryption. AES symmetric encryption is employed to secure the business data before cloud storage. Experimental evaluation was performed using the FVC2002 fingerprint dataset and Mozilla CV-Corpus audio dataset. The generated biometric master-key successfully passed Shannon Entropy, Chi-Square, Monte Carlo Pi, and decryption validation tests, demonstrating improved randomness, reliability, and resistance against unauthorized access. The proposed framework effectively eliminates single-point failure issues and enhances secure cloud data access through threshold-based biometric authentication.