Cloud Computing (CC) is a technology that is growing at a faster pace, gaining much popularity with startup companies increasing and opting for clouds’ services. The growth rate of this technology is high and so is the security threat. Though there are existing authentication systems like two factor authentication, biometric based authentication and so on, hackers still pose greater challenges. Though password-based authentication system is simple, it is highly vulnerable to security breaches. Biometric based authentication has better security, but usage of single biometric traits can also be easily compromised. Thus, this research work, Multimodal Biometric Hashkey Cryptography (MBHC) brings about a novel security framework that focuses on both authentication and securing cloud data using multiple modalities. Authentication is based on multiple biometric modalities viz. Fingerprint, Iris and Face features. Features from these modalities are extracted using linear filters. Artificial Fish Swarm Algorithm (AFSA) is used for feature optimization, further to which Support Vector Machine (SVM) acts as classifier for identifying genuine users against imposters. Cryptographic keys are generated from these modalities that serve as input to Advanced Encryption Standard (AES) Algorithm for encrypting and decrypting the information stored in cloud. Integrity of data is also ensured using hash function for which the facial features and the encrypted message are the inputs. Thus, the experimental results show that the proposed architecture provides improved security for cloud computing environment when compared to the existing models.